Vocational ability dynamic portrait and post recommendation method and system

Abstract

The invention relates to the technical field of post recommendation, and particularly discloses a vocational ability dynamic portrait and post recommendation method and system. The method comprises the steps of firstly collecting static data (work resumes, assessment scores and the like) and dynamic behavior data (project tasks, skill training and the like) of a user, laying a comprehensive data source foundation, then disassembling the work resumes, extracting and clustering multiple types of entities, screening and determining a core capability field label in combination with time, secondly quantifying core capability scores and a performance stability coefficient, and finally obtaining a core capability model. Then analyzing dynamic behavior data, extracting evolution characteristics, constructing dynamic capability evolution vectors, fusing the dynamic capability evolution vectors with the static vectors to form vocational capability dynamic portraits, analyzing post demand data, extracting capability requirements and environment characteristics, quantifying the post demand data and the capability requirements and environment characteristics, and carrying out model processing to generate post portrait vectors; and finally, comprehensive suitability is calculated through the cosine similarity and the growth trend matching degree, candidate posts are sorted and recommended, and the integrating degree of the posts and talents is effectively improved.

Description

Technical Field

[0001] This invention relates to the field of job recommendation technology, and in particular to a dynamic profiling of professional abilities and a method and system for job recommendation. Background Technology

[0002] In today's digital and rapidly changing job market, achieving accurate and efficient matching of talent and jobs is crucial for individual career development and corporate talent strategy. However, existing job recommendation methods mainly rely on keyword matching between static resumes manually filled out by job seekers and job descriptions posted by employers. Static resumes are outdated and cannot reflect the latest skills, project experience, and professional growth of job seekers, resulting in outdated and one-sided competency profiles and low matching rates in the recommendations. Therefore, a dynamic competency profile and job recommendation method is needed to solve the above problems. Summary of the Invention

[0003] The purpose of this invention is to provide a method for dynamic profiling of professional abilities and job recommendation, including: A method for dynamic profiling of professional abilities and job recommendation includes: Acquire user occupational data within a preset time period, including static data and dynamic behavioral data; Work history information and professional behavior information are obtained from the static data, and work named entities and keywords are obtained from the work history information, and core competency domain tags are obtained from the work named entities and keywords. A static capability vector is obtained based on the core capability domain tags and the professional behavior information; Based on the dynamic behavioral data, obtain behavioral sequences and behavioral quantification indices, and based on the behavioral sequences and behavioral quantification indices, obtain dynamic capability evolution vectors, and based on the dynamic capability evolution vectors and static capability vectors, construct a dynamic profile of professional capabilities. Obtain user job requirement data, and obtain job competency requirement semantic vector and job context feature value based on the job requirement data, and construct job profile based on the job competency requirement semantic vector and the job context feature value; The job profile is matched with the job profile to obtain job matching information, and job recommendations are made to the user based on the job matching information.

[0004] Preferably, the steps of obtaining job named entities and keywords based on the job resume information, and obtaining core competency domain tags based on the job named entities and keywords, include: The work history information is semantically deconstructed to obtain semantic segments for the company where the employee works, job responsibilities, and projects undertaken. The corresponding set of company organizational entities is obtained based on the semantics of the company segment, wherein the company organizational entities include semantic terms for company operation and semantic terms for business unit, and the company organizational entities are used as job naming entities; Semantic analysis is performed on the job responsibility section to obtain multiple verb-noun phrase combinations of core job functions. These verb-noun phrases of core job functions are then combined to form a set of job responsibility keywords, and the verb-noun phrases of core job functions are used as keywords. Obtain the set of skill entities based on the semantics of the job responsibility section; Obtain the project entity set based on the semantics of the project segment you are responsible for; The set of company organizational entities, the set of skill entities, the set of project entities, and the set of responsibility keywords are merged and deduplicated to form an initial set of capability elements; The initial set of capability elements is clustered based on a clustering algorithm to obtain multiple capability clusters, and the semantic center vector of each capability cluster is obtained. Obtain the time information associated with the original entity in each capability cluster, obtain multiple timestamps corresponding to the multiple time information, construct a time series based on the multiple timestamps, filter the multiple semantic center vectors according to the time series based on the closest time to the current time to obtain the nearest semantic center vector, and obtain the core capability domain label based on the nearest semantic center vector.

[0005] Preferably, the step of obtaining a static capability vector based on the core capability domain label and the professional behavior information includes: Based on the core competency domain tags, obtain multiple core competency assessment scores of the user within a preset time period, and calculate core competency score feature values ​​based on the multiple core competency assessment scores; Based on the professional conduct information, professional conduct characteristic items are obtained, wherein the professional conduct characteristic items include the frequency of professional conduct and the duration of professional conduct. The employment stability coefficient is obtained based on the frequency of practice and the duration of practice. The core competency score feature value and the professional stability coefficient are concatenated according to a preset time series to obtain a professional competency vector, and the professional competency vector is used as a static competency vector.

[0006] Preferably, the step of obtaining a dynamic capability evolution vector based on the behavior sequence and the behavior quantification index, and constructing a dynamic profile of professional capabilities based on the dynamic capability evolution vector and the static capability vector, includes: Multiple behavioral events and their corresponding timestamps are obtained based on the behavioral sequence, and multiple evolution intervals between the behavioral events and their adjacent behavioral events are obtained based on the multiple behavioral events and the multiple timestamps. Based on the behavior quantification index, obtain multiple behavior quantification sub-indices corresponding to each behavior event, and calculate the comprehensive quantification value of each behavior event based on the multiple behavior quantification sub-indices; Behavioral evolution feature values ​​within multiple time windows are calculated sequentially based on multiple behavioral temporal features, multiple evolution intervals, and multiple comprehensive quantization values; Time-series regression analysis was performed on multiple behavioral evolution characteristic values ​​to obtain behavioral trend indicators and behavioral fluctuation coefficients. Obtain the baseline behavioral evolution feature value of the user within the same historical time period, and calculate the behavioral evolution difference value based on the behavioral evolution feature value and the baseline behavioral evolution feature value; The behavioral trend index, the behavioral fluctuation coefficient, and the behavioral evolution difference value are vectorized and concatenated to obtain the dynamic capability evolution vector. The dynamic capability evolution vector and the static capability vector are weighted, fused, and normalized to form the dynamic profile of professional capabilities.

[0007] Preferably, the step of obtaining the job competency requirement semantic vector and job context feature value based on the job requirement data, and constructing a job profile based on the job competency requirement semantic vector and the job context feature value, includes: The job requirement data is structured and parsed to obtain a job description text segment, a job requirement text segment, and an organizational environment text segment; Semantic analysis is performed on the job description text segment and the job requirement text segment to extract multiple capability keyword entities. The capability keyword entities are then mapped to the corresponding capability dimensions according to a preset capability classification system to form an initial set of job capability requirements. Multiple organizational feature entities are extracted from the organizational environment text segment. The organizational feature entities include industry type, team size and business maturity tags, which constitute an initial set of job context feature values. Obtain the entity weights for each capability dimension in the initial set of job capability requirements, wherein the entity weights are calculated based on the frequency of the entity's occurrence in the text and a preset importance coefficient; The features of each organizational feature entity in the initial job context feature value set are quantized to obtain the corresponding job context feature value; Obtain a preset initial value for the weight of each entity, calculate multiple capability requirement intensity values ​​based on multiple preset initial values ​​and multiple entity weights, and merge the multiple capability requirement intensity values ​​to generate a job capability requirement semantic vector; The job context feature value and the job competency requirement semantic vector are concatenated and input into a preset job profile generation model for feature fusion and dimensionality reduction, and a structured job profile vector is output.

[0008] Preferably, the step of matching the dynamic profile of professional abilities with the job profile to obtain job matching information, and recommending jobs to users based on the job matching information, includes: Obtain the corresponding dynamic profile vector of professional ability based on the dynamic profile of professional ability; Obtain the corresponding job requirement vector based on the job profile; Based on cosine similarity, the vector similarity between the dynamic profile vector of professional ability and the vector of job requirements is calculated to obtain the ability matching score; Extract behavioral trend indicators and behavioral fluctuation coefficients from the dynamic profile of professional competence; Obtain the preset growth adaptability weights in the job profile, and calculate the growth trend matching degree based on the growth adaptability weights, the behavioral trend indicators, and the behavioral fluctuation coefficient; The ability matching score and the growth trend matching score are normalized to obtain normalized values ​​for the ability matching score and growth trend matching score. The normalized values ​​for the ability matching score and growth trend matching score are then weighted and fused to generate a comprehensive job matching score. Multiple candidate positions are obtained based on the job profile, and the multiple candidate positions are sorted according to the overall job matching degree to obtain a job recommendation list; Job recommendations are made to users based on the job recommendation list.

[0009] This application also provides a dynamic professional competence profiling and job recommendation system, including: The data acquisition module is used to acquire users' occupational data within a preset time period, including static data and dynamic behavioral data. The static capability modeling module is used to obtain work history information and professional behavior information based on the static data, obtain job named entities and keywords based on the work history information, and obtain core capability domain tags based on the job named entities and keywords. The dynamic capability evolution module is used to obtain a static capability vector based on the core capability domain labels and the professional behavior information. The professional competence profile synthesis module is used to obtain behavior sequences and behavior quantification indices based on the dynamic behavior data, obtain dynamic competence evolution vectors based on the behavior sequences and behavior quantification indices, and construct a dynamic professional competence profile based on the dynamic competence evolution vectors and static competence vectors. The job profile building module is used to obtain the user's job requirement data, obtain the job competency requirement semantic vector and job context feature value based on the job requirement data, and build a job profile based on the job competency requirement semantic vector and the job context feature value. The job recommendation module is used to match the dynamic profile of professional abilities with the job profile to obtain job matching information, and to recommend jobs to users based on the job matching information.

[0010] Preferably, the static capability modeling module includes: The resume semantic deconstruction unit is used to deconstruct the work resume information semantically to obtain the semantics of the company where the employee works, the semantics of the job responsibilities section, and the semantics of the projects the employee is responsible for. The company organization entity extraction unit is used to obtain the corresponding set of company organization entities based on the semantics of the company segment, wherein the company organization entities include company operation semantic terms and business unit semantic terms, and the company organization entities are used as job naming entities; The responsibility keyword extraction unit is used to perform semantic analysis on the semantics of the job responsibility segment, obtain multiple verb-noun phrase combinations of core job functions, form a responsibility keyword set from the multiple verb-noun phrases of the core job functions, and use the verb-noun phrases of the core job functions as keywords; The skill entity extraction unit is used to obtain a set of skill entities based on the semantics of the job responsibility segment. The project entity extraction unit is used to obtain a set of project entities based on the semantics of the project segment it is responsible for; The capability element set construction unit is used to merge and deduplicatize the company organization entity set, the skill entity set, the project entity set, and the responsibility keyword set to form an initial capability element set. A capability element clustering unit is used to cluster the initial set of capability elements based on a clustering algorithm to obtain multiple capability clusters and to obtain the semantic center vector of each capability cluster. The time series filtering unit is used to obtain the time information associated with the original entity in each capability cluster, obtain multiple timestamps corresponding to the multiple time information, construct a time series based on the multiple timestamps, filter the multiple semantic center vectors according to the time series based on the closest time to the current time to obtain the nearest semantic center vector, and obtain the core capability domain label based on the nearest semantic center vector.

[0011] This application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method.

[0012] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0013] The beneficial effects of this application are as follows: This invention first collects static user data (work history, performance evaluation, etc.) and dynamic behavioral data (project tasks, skills training, etc.) to lay a comprehensive data source foundation. Then, it deconstructs the work history, extracts multiple types of entities and clusters them. Combined with time filtering, it determines the core competency domain labels. Next, it quantifies the core competency score and the job stability coefficient, splices them into a static competency vector, and then analyzes the dynamic behavioral data, extracts evolutionary features and constructs a dynamic competency evolution vector. This vector is then fused with the static vector to form a dynamic profile of professional competence. At the same time, it analyzes job requirement data, extracts competency requirements and environmental features, quantifies them, and generates a job profile vector through model processing. Finally, it calculates the comprehensive suitability through cosine similarity and growth trend matching degree, sorts candidate jobs and recommends them, effectively improving the fit between jobs and talents. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of a method flow according to an embodiment of this application.

[0015] Figure 2 This is a schematic diagram of the system structure according to an embodiment of this application.

[0016] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0017] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0018] like Figure 1 As shown, this application provides a method for dynamic profiling of professional abilities and job recommendation, including: A method for dynamic profiling of professional abilities and job recommendation includes: S1. Obtain the user's occupational data within a preset time period, whereby the occupational data includes static data and dynamic behavioral data; S2. Obtain work history information and professional behavior information based on the static data, and obtain job named entities and keywords based on the work history information, and obtain core competency domain tags based on the job named entities and keywords; S3. Obtain a static capability vector based on the core capability domain tags and the professional behavior information; S4. Obtain a behavior sequence and behavior quantification index based on the dynamic behavior data, obtain a dynamic ability evolution vector based on the behavior sequence and behavior quantification index, and construct a dynamic profile of professional ability based on the dynamic ability evolution vector and static ability vector. S5. Obtain the user's job requirement data, and obtain the job competency requirement semantic vector and job context feature value based on the job requirement data, and construct a job profile based on the job competency requirement semantic vector and the job context feature value. S6. Match the dynamic profile of professional ability with the job profile to obtain job matching information, and recommend jobs to users based on the job matching information.

[0019] As described in steps S1-S6 above, existing job recommendation methods primarily rely on keyword matching between static resumes manually filled out by job seekers and job descriptions posted by employers. However, static resumes are often outdated and fail to reflect the job seeker's latest skills, project experience, and professional growth, resulting in outdated and incomplete competency profiles and low relevance of the recommendations. Step S1 of this invention forms the foundation of the entire technical process, with its core objective being to acquire professional data within a preset timeframe. This data is divided into static data and dynamic behavioral data. Static data is primarily obtained through previously filled-out professional profiles and records from corporate human resources systems, covering relatively stable information such as work experience, professional qualifications, and performance evaluations. Dynamic behavioral data originates from real-time or recent information such as user activity on career-related platforms, behavioral records during project execution, and learning behavior on skills learning platforms. Examples include project tasks participated in by the user in the past six months, completed skills training, and frequency of operations on work platforms. The physical significance of this step lies in providing a comprehensive and authentic data source for subsequent profile construction. Only by acquiring sufficiently rich static basic information and dynamic change information can we break through the limitations of relying solely on static resumes and lay the foundation for building a dynamic and comprehensive competency profile. By collecting both types of data simultaneously, we can grasp both the stable professional skills that users have already developed and capture the latest trends in changes to those skills, avoiding the one-sidedness of the profile caused by limited data.

[0020] Step S2, based on the static data obtained in S1, further mines the effective information within it to obtain core competency domain labels. First, the work history information in the static data undergoes semantic structuring and deconstruction. This process employs semantic parsing techniques from natural language processing to break down the work history text into three core parts: the semantics of the company where the employee works, the semantics of the job responsibilities, and the semantics of the projects the employee is responsible for. This ensures a refined analysis of the resume information. Subsequently, for the semantic analysis of the company section, entity recognition algorithms are used to extract semantic terms related to company operations and business units, forming a set of company organizational entities that serve as named entities for the job. For example, entities such as "Internet Company" and "E-commerce Business Unit" are extracted from "E-commerce Business Unit of an Internet Company." These entities reflect the user's industry background and business area. When performing semantic analysis on the job responsibility section, phrase extraction algorithms are used to obtain verb-noun phrase combinations of multiple core job functions, such as "data analysis," "project management," and "customer liaison." These phrases directly reflect the user's job content and constitute a set of responsibility keywords. Simultaneously, a set of skill entities is extracted from the job responsibility section, such as "Python programming," "Excel data visualization," and "communication and coordination." A set of project entities is extracted from the project section, such as "e-commerce platform upgrade project" and "user growth optimization project." Afterward, the company organizational entity set, skill entity set, project entity set, and responsibility keyword set are merged and deduplicated to form an initial set of capability elements. This step integrates various capability-related information scattered throughout the work resume, avoiding omissions. Next, the initial set of capability elements is clustered using the K-means clustering algorithm. Elements with similar semantics and close associations are grouped together to form multiple capability clusters. The semantic center vector of each capability cluster is obtained by calculating the average of the semantic vectors of all elements in each cluster. For example, elements such as "Python programming," "data analysis," and "data visualization" are clustered into the "data processing capability cluster," and its semantic center vector can represent the core semantics of this cluster. Finally, the time information associated with the original entities in each capability cluster is obtained, such as the time of skill acquisition and the execution time of projects. This time information is converted into corresponding timestamps and a time series is constructed. Based on the time series, the nearest semantic center vectors to the current time are selected to determine the core capability domain label. For example, if the timestamps of the related entities in the "data processing capability cluster" are all within the last year, while the timestamps of the related entities in the "customer interaction capability cluster" are mostly three years ago, then "data processing" is prioritized as the core capability domain label.The physical significance of this step lies in accurately extracting the user's core competency domains from static work history information. Through techniques such as semantic deconstruction, entity extraction, cluster analysis, and time-series filtering, it ensures that the core competency domain tags can truly reflect the user's current main competency direction, providing a key basis for the subsequent construction of static competency vectors. Its technical contribution lies in achieving effective classification of competency elements through clustering algorithms and ensuring the timeliness of core competency tags through time-series filtering, avoiding the subjectivity and lag in the judgment of competency domains in traditional methods.

[0021] The core of step S3 is to obtain a static competency vector based on core competency domain labels and professional behavior information. First, based on the core competency domain labels determined in S2, multiple core competency assessment scores of the user within a preset time period are obtained from channels such as enterprise performance appraisal systems and professional assessment platforms. For example, for the core competency domain of "data processing," the user's two most recent assessment scores are 85 and 92 points, respectively. Then, a weighted average algorithm is used to calculate the core competency score feature value. If the preset weight of recent scores is higher, such as a weight of 0.6 for recent scores and 0.4 for previous scores, then the core competency score feature value is 85 × 0.4 + 92 × 0.6 = 89.2 points. This feature value quantifies the user's current level in the core competency domain. Secondly, professional behavior characteristics are extracted from static data on professional conduct, including frequency of practice and duration of practice. Frequency of practice refers to the number of times a user participates in work-related activities within a preset time period, such as project participation and business processing. Duration of practice refers to the continuous duration of practice in the user's current or past positions. Based on these two characteristics, a preset formula is used: "Professional Stability Coefficient = Duration of Practice × 0.7 + ... The occupational stability coefficient is calculated by multiplying the score by 0.3. This formula normalizes the frequency of employment using the sigmoid function and then weights it with the employment time span. The occupational stability coefficient reflects the stability of a user's professional behavior. Finally, the core competency score feature values ​​and the occupational stability coefficient are concatenated according to a preset time series, for example, in the order of "core competency score feature values ​​of the past year - occupational stability coefficient of the past year - core competency score feature values ​​of the past two years - occupational stability coefficient of the past two years," forming an occupational competency vector, which is then used as a static competency vector. The physical significance of this step lies in quantifying and integrating the user's core competency level and occupational behavior stability to form vector data that reflects the user's current stable professional competency, providing a static foundation for the subsequent construction of a dynamic professional competency profile. Its technical contribution lies in making the representation of static competency more objective and comprehensive by combining quantitative scores and stability coefficients, avoiding the limitations of traditional methods that evaluate competency using only a single indicator. At the same time, the preset time series concatenation method retains competency-related information from different time periods, providing data structure support for subsequent fusion with the dynamic competency evolution vector.

[0022] Step S4 is crucial for constructing a dynamic profile of professional competence. It aims to obtain dynamic competence evolution vectors from dynamic behavioral data and integrate them with static competence vectors to form a complete profile. First, based on the dynamic behavioral data obtained in S1, behavioral sequences and behavioral quantification indices are extracted. A behavioral sequence is an ordered set of career-related behaviors that occur chronologically within a preset time period, such as "participating in skills training - completing project tasks - submitting results reports - receiving feedback and evaluation." Behavioral quantification indices are quantitative indicators of the quality of completion and level of engagement for each behavior. Based on the behavioral sequences, timestamps corresponding to each behavioral event are obtained through timestamp extraction technology. The evolution interval between adjacent behavioral events is then calculated. For example, if "participating in skills training" occurs on day 10 and "completing project tasks" occurs on day 30, the evolution interval is 20 days. This evolution interval reflects the temporal distribution characteristics of the user's competence-related behaviors. Secondly, multiple sub-indices corresponding to each behavioral event are extracted from the behavioral quantitative index. For example, in skills training, these include "attendance time percentage," "assessment pass rate," and "knowledge point mastery," while in project tasks, they include "completion timeliness," "result qualification rate," and "collaboration satisfaction." The comprehensive quantitative value of each behavioral event is calculated using the weighted summation formula "Comprehensive Quantitative Value = Σ (Sub-index × Corresponding Weight)." The weights are preset based on the type of behavior and the importance of the sub-index. For example, in skills training, the weight of "assessment pass rate" is set to 0.4, "knowledge point mastery" to 0.3, and "attendance time percentage" to 0.3. If a user's three sub-indices for this training are 0.9, 0.8, and 1.0, respectively, then the comprehensive quantitative value is 0.9 × 0.4 + 0.8 × 0.3 + 1.0 × 0.3 = 0.36 + 0.24 + 0.3 = 0.9. The comprehensive quantitative value can fully reflect the quality level of a single behavioral event. Next, multiple time-series windows of fixed duration are preset, such as 15 days per window. The behavioral time-series characteristics (e.g., the temporal distribution density of behavior occurrences), evolution intervals, and comprehensive quantitative values ​​within each window are combined sequentially. A feature fusion algorithm is used to calculate the behavioral evolution characteristic value for each time-series window. This value reflects the evolutionary state of user behavior within a specific time period. Time-series regression analysis is performed on the behavioral evolution characteristic values ​​of all time-series windows. A linear regression model is used to fit the trend of characteristic values ​​over time, obtaining behavioral trend indicators (e.g., characteristic value growth rate and growth magnitude) and behavioral fluctuation coefficients (e.g., the standard deviation of characteristic values ​​from the fitted curve). The behavioral trend indicators reflect the overall development direction of user ability-related behaviors, while the behavioral fluctuation coefficient reflects the stability of behavioral evolution. Simultaneously, the baseline behavioral evolution characteristic values ​​of users in the same historical period are retrieved from the historical database, such as the behavioral evolution characteristic values ​​of the corresponding time-series window from the same period last year. The difference between the current behavioral evolution characteristic value and the baseline value is calculated as the behavioral evolution difference value, which reflects the change in the user's current ability evolution compared to the same historical period.Subsequently, the behavioral trend indicators, behavioral fluctuation coefficients, and behavioral evolution difference values ​​are vectorized, and a dynamic capability evolution vector is formed through a vector concatenation algorithm. This vector can comprehensively capture the evolutionary characteristics of user capabilities over time. Finally, a weighted fusion algorithm is used to fuse the dynamic capability evolution vector with the static capability vector obtained in S3. The fusion weight is preset according to the application scenario. For example, for growth-oriented positions, the weight of the dynamic capability evolution vector is set to 0.6, and the weight of the static capability vector is set to 0.4. For stable positions, the opposite is true. After fusion, the vector values ​​are normalized to map to the [0,1] interval, ultimately forming a dynamic profile of professional capabilities. The physical significance of this step lies in breaking through the limitations of traditional static capability profiles, dynamically capturing the changing trends and evolutionary patterns of user capabilities, and enabling the capability profile to reflect the latest capability status of users in real time. Its technical contribution lies in transforming scattered dynamic behavioral data into evolution vectors with clear physical meaning through algorithms such as time-series window analysis and regression modeling. The fusion with the static capability vector achieves a comprehensive characterization of the "current status" and "trend" of user capabilities, providing richer capability dimension information for subsequent accurate matching.

[0023] The goal of step S5 is to build accurate job profiles based on user job requirement data, providing a clear reference standard for subsequent matching. First, the job requirement data is structured and parsed. A text segmentation algorithm is used to break down the job description text into responsibility description, job requirements, and organizational environment segments. The responsibility description segment clarifies the core job content, the job requirements segment specifies the job's requirements for the applicant's abilities, education, experience, etc., and the organizational environment segment describes the industry background, team size, business maturity, and other environmental characteristics of the job. Second, semantic analysis is performed on the responsibility description and job requirements segments. A keyword entity extraction algorithm (such as TF-IDF combined with an entity recognition model) is used to extract multiple capability keyword entities, such as "machine learning," "3+ years of project management experience," and "cross-departmental communication." Then, according to a pre-defined capability classification system (e.g., divided into three categories: professional technical skills, general skills, and management skills, with each category further subdivided into specific dimensions), these capability keyword entities are mapped to the corresponding capability dimensions, forming an initial set of job capability requirements. For example, "machine learning" is mapped to the "professional technical skills - algorithm skills" dimension, and "cross-departmental communication" is mapped to the "general skills - communication and coordination skills" dimension. Simultaneously, feature extraction is performed on the organizational environment text segment to obtain organizational feature entities such as industry type (e.g., Internet, Finance, Manufacturing), team size (e.g., less than 10 people, 10-50 people, more than 50 people), and business maturity tags (e.g., startup, growth, maturity), which constitute the initial set of job context feature values. Next, the entity weights under each capability dimension in the initial set of job capability requirements are calculated. The entity weights are determined by the frequency of the entity's appearance in the text and a preset importance coefficient. The calculation method is "Entity weight = (Frequency of appearance / Total number of entities) × Importance coefficient". The importance coefficient is preset according to the job type. For example, the importance coefficient of entities related to "professional technical ability" in technical positions is set to 0.7, and the importance coefficient of entities related to "general ability" is set to 0.3. If the entity "Python programming" in a certain technical position appears 3 times, the total number of entities is 20, and the importance coefficient is 0.7, then its weight is (3 / 20) × 0.7 = 0.105. The organizational feature entities in the initial set of job context feature values ​​are quantified. For example, the industry type is mapped to a numerical code (Internet = 1, Finance = 2, Manufacturing = 3), the team size is mapped to quantified values ​​of 1, 2, 3, etc. according to the interval, and the business maturity label is mapped to quantified values ​​of 0.3, 0.6, 0.9, etc. according to the development stage, to obtain the corresponding job context feature values.Then, a preset initial value for the weight of each entity is obtained (set according to industry-standard norms). The capability requirement intensity value for each capability dimension is calculated based on the entity weight, using the formula: "Capability Requirement Intensity Value = Preset Initial Value × Entity Weight × 10". The capability requirement intensity values ​​for all capability dimensions are merged in a preset order to generate a job capability requirement semantic vector. This vector quantifies the degree of requirement for each capability dimension. Finally, the job context feature values ​​are concatenated with the job capability requirement semantic vector to form a comprehensive vector integrating capability requirements and environmental features. This vector is then input into a preset job profile generation model (built based on a neural network, containing an input layer, hidden layers, and an output layer, where the hidden layer uses the ReLU activation function and the output layer uses the Sigmoid activation function) for feature fusion and dimensionality reduction. Using the optimal parameters determined during model training (e.g., 64 hidden layer neurons, a learning rate of 0.001, and 100 iterations), a structured job profile vector is output, completing the construction of the job profile. The physical significance of this step lies in transforming unstructured job requirement data into structured, quantifiable vector data, comprehensively capturing the job's competency requirements and environmental adaptability characteristics. Its technical contribution lies in breaking through the limitations of traditional job analysis, which only stays at the keyword level, through structured analysis, feature quantification, and model dimensionality reduction. This makes job profiles more accurate and calculable, providing a scientific reference for subsequent matching with dynamic professional competency profiles.

[0024] Step S6 is the final step in job recommendation. It achieves accurate recommendations through multi-dimensional matching of the dynamic profile of user skills and the job profile. First, it extracts the corresponding dynamic profile vector of user skills from the dynamic profile constructed in S4. This vector contains comprehensive information on the user's static abilities and the evolution of their dynamic abilities. Then, it extracts the corresponding job requirement vector from the job profile constructed in S5. This vector contains the strength of the job's ability requirements and quantitative information on contextual features. Next, it calculates the similarity between the dynamic profile vector of user skills and the job requirement vector using a cosine similarity algorithm. The formula for cosine similarity is "cosθ=(A·B) / (||A||×||B||)", where A is the dynamic profile vector of user skills and B is the job requirement vector. The result is the ability matching score, which reflects the degree of fit between the user's current abilities and the job's ability requirements. A higher score indicates a stronger ability fit. Then, the behavioral trend index and behavioral fluctuation coefficient calculated by S4 are extracted from the dynamic profile of professional competence. Simultaneously, the preset growth adaptability weights in the job profile are obtained (set according to the job's growth potential and the requirements for the job seeker's development potential; for jobs with large growth potential, the growth adaptability weight is set to 0.6-0.8, and for jobs with small growth potential, it is set to 0.2-0.4). The growth trend matching degree is calculated using the formula "Growth Trend Matching Degree = Behavioral Trend Index × Growth Adaptability Weight + (1 - Behavioral Fluctuation Coefficient) × (1 - Growth Adaptability Weight)". This index reflects the degree of fit between the user's competence growth trend and the job's requirements for the job seeker's development potential. Next, the competency matching score and growth trend matching score are normalized using the Min-Max normalization method, mapping both indicators to the [0,1] interval to obtain normalized values ​​for the competency matching score and growth trend matching score. The weights of these two indicators are preset based on job type and company hiring preferences. For example, for mature positions, the weight of the normalized competency matching score is set to 0.7, and the weight of the normalized growth trend matching score is set to 0.3; the opposite is true for startup positions. The weighted sum is used to obtain the overall job matching score, which comprehensively reflects the degree of fit between the user and the job in terms of current competency and growth potential. Then, based on the job profile, multiple candidate positions meeting basic requirements are selected from the job database and sorted according to the overall job matching score from highest to lowest, forming a job recommendation list. Positions with higher overall matching scores are ranked higher. Finally, the job recommendation list is presented to the user, completing the job recommendation process.The physical significance of this step lies in accurately matching user capabilities with job requirements through multi-dimensional matching calculations, thereby selecting the most suitable jobs for users. Its technical contribution lies in breaking through the limitations of traditional single keyword matching. It achieves accurate matching of capability dimensions through cosine similarity calculation, and takes into account user development potential and job growth requirements through growth trend matching calculation. The comprehensive matching degree after weighted fusion makes the recommendation results more scientific and comprehensive, effectively improving the fit of job recommendations, and thus better meeting the needs of accurate matching of talent and jobs in the digital employment market.

[0025] In one embodiment, step S2, which involves obtaining job-named entities and keywords based on the job resume information and obtaining core competency domain tags based on the job-named entities and keywords, includes: S21. Perform semantic structure deconstruction on the work history information to obtain semantics for the company segment, semantics for the job responsibility segment, and semantics for the project segment. S22. Obtain the corresponding set of company organizational entities based on the semantics of the company segment, wherein the company organizational entities include company operation semantic terms and business unit semantic terms, and use the company organizational entities as job naming entities; S23. Perform semantic analysis on the semantics of the job responsibility section to obtain multiple verb-noun phrase combinations of core job functions, form a set of responsibility keywords from the multiple verb-noun phrases of the core job functions, and use the verb-noun phrases of the core job functions as keywords; S24. Obtain the skill entity set based on the semantics of the job responsibility section; S25. Obtain the project entity set based on the semantics of the project segment under your responsibility; S26. Merge and deduplicatize the set of company organizational entities, the set of skill entities, the set of project entities, and the set of responsibility keywords to form an initial set of capability elements. S27. Based on the clustering algorithm, the initial set of capability elements is clustered to obtain multiple capability clusters, and the semantic center vector of each capability cluster is obtained. S28. Obtain the time information associated with the original entity in each capability cluster, obtain multiple timestamps corresponding to the multiple time information, construct a time series based on the multiple timestamps, filter the multiple semantic center vectors according to the time series based on the closest current time to obtain the nearest semantic center vector, and obtain the core capability domain label based on the nearest semantic center vector.

[0026] As described in steps S21-S28 above, step S21 of this invention serves as the starting point of the entire process and aims to semantically deconstruct the work resume information. This step employs semantic parsing technology from natural language processing, combined with the inherent structural features of the resume text, to break down the complete work resume text into three core parts: the company section semantics, the job responsibility section semantics, and the project section semantics. The company section semantics mainly covers information such as the name of the company the user has worked for or is currently working for, the industry, and the business unit, reflecting the user's professional background from a macro perspective. The job responsibility section semantics details the user's specific job content, responsibilities, and tasks in their position, and is the core part for uncovering the user's skills and work abilities. The project section semantics records information such as the name of the project the user participated in or led, the project type, and project responsibilities, reflecting the user's application of abilities in actual projects. The physical significance of this step lies in breaking down the holistic and unstructured nature of work resume texts, dividing scattered information according to semantic categories, and laying the foundation for subsequent targeted entity extraction. Technically, this is achieved through pre-defined semantic rules and text segmentation algorithms, ensuring clear boundaries and complete content for each semantic segment after segmentation. For example, a resume containing the statement "Worked in the AI ​​division of a technology company from 2020 to 2023, primarily responsible for training and optimizing machine learning models, and led the intelligent recommendation system upgrade project," can be accurately segmented into the company segment "Worked in the AI ​​division of a technology company from 2020 to 2023," the job responsibility segment "primarily responsible for training and optimizing machine learning models," and the project segment "led the intelligent recommendation system upgrade project." This structured decomposition makes subsequent information extraction more targeted, improving the efficiency and accuracy of data processing.

[0027] Step S22, based on the semantics of the company segment obtained from S21, acquires the corresponding set of company organizational entities and uses them as job naming entities. This step employs entity recognition algorithms to identify and extract industry-related and organizational structure-related terms from the semantics of the company segment. Specifically, company organizational entities include company operational semantic terms and business unit semantic terms. Company operational semantic terms mainly reflect the company's business areas and operating direction; for example, extracting the operational semantic term "e-commerce" from "a certain e-commerce company." Business unit semantic terms reflect the company's internal organizational structure and business sub-sectors; for example, extracting the business unit semantic term "artificial intelligence business unit" from "artificial intelligence business unit." These extracted sets of company organizational entities, used as job naming entities, can provide a reference for judging the user's skill area from the perspective of industry background and organizational environment. For example, if a user's job naming entity contains "e-commerce" and "artificial intelligence business unit," it can be preliminarily determined that their skill area may be related to artificial intelligence applications in the e-commerce industry. The physical significance of this step lies in transforming the key organizational information in the semantics of the company segment into structured entity data, providing industry and organizational dimension support for the subsequent integration of capability elements. Its technical implementation is achieved through a trained entity recognition model (fine-tuned based on a BERT pre-trained model, with the model input being the semantic text of the company segment and the output being the set of identified entities), ensuring the accuracy of entity extraction and avoiding the omission of key organizational information. At the same time, by clarifying the specific types of company organizational entities, the extracted entities are made more targeted and have greater application value.

[0028] Step S23 performs semantic analysis on the job responsibility segment obtained in S21, extracting verb-noun phrase combinations of core job functions as keywords to form a set of responsibility keywords. This step first uses a syntactic analysis algorithm to perform grammatical structure analysis on the job responsibility segment, identifying verb and noun components. Then, it uses phrase extraction rules to filter out verb-noun phrase combinations that reflect core job functions. These phrase combinations directly correspond to the user's specific job content and functional direction, and are core information reflecting the user's capabilities. For example, from the job responsibility segment "responsible for customer needs coordination, project progress tracking, and deliverables," verb-noun phrase combinations such as "customer needs coordination," "project progress tracking," and "deliverables" can be extracted. These phrases together constitute the set of responsibility keywords. The physical significance of this step lies in transforming abstract job descriptions into specific, quantifiable keyword information that directly relates to the user's work capabilities and functional scope. Technically, this is achieved through a pre-set core functional phrase library and syntactic analysis rules, ensuring that the extracted phrases accurately reflect core job functions and avoiding the extraction of irrelevant or secondary phrases. For example, expressions that lack clear functional orientation, such as "assisting in completing daily tasks," are excluded, making the set of job keyword sets more representative and providing core functional dimension support for the subsequent integration of capability elements.

[0029] Step S24 continues to obtain a set of skill entities based on the semantics of the job responsibility section. This step uses a skill keyword recognition algorithm, combined with a pre-set skill dictionary (covering common professional skills and general skill vocabulary in various industries), to extract skill-related entity information from the semantics of the job responsibility section. Skill entities include both professional technical skills, such as extracting "Python programming," "data analysis," and "data visualization" from "using Python for data analysis and visualization," and general skills, such as extracting "cross-departmental coordination" and "communication skills" from "coordinating cross-departmental resources and solving communication problems in projects." These skill entities directly reflect the user's actual application capabilities at work and are key elements constituting the user's core competencies. The physical significance of this step lies in accurately mining the skill support behind job responsibilities and associating job content with specific skills. Its technical implementation combines skill dictionary matching and semantic similarity calculation, which not only ensures the accurate extraction of common skills but also identifies some emerging or industry-specific skill terms. For example, for the statement "model optimization based on Transformer architecture," it can accurately extract skill entities such as "Transformer architecture application" and "model optimization," enriching the coverage of the skill entity set and providing a basis for the subsequent judgment of core competency areas.

[0030] Step S25, based on the semantics of the project segments obtained from S21, yields a set of project entities. This step employs a project keyword recognition algorithm to extract relevant entity information such as project name, project type, and core project objectives from the project segment semantics. For example, from the project segment semantics of "leading an e-commerce platform user growth optimization project, improving user retention rate through algorithm iteration," project entities such as "e-commerce platform user growth optimization project," "algorithm iteration," and "improving user retention rate" can be extracted. The set of project entities reflects the project background, scale, and core tasks undertaken by the user, indirectly reflecting the user's ability level and application scenarios. For instance, users who participate in large and complex projects typically possess stronger project management and professional technical skills. The physical significance of this step lies in transforming project experience into structured entity information, providing project practice dimension support for judging user capabilities. Its technical implementation, through a project feature lexicon and semantic analysis algorithm, ensures that the extracted project entities accurately reflect the core attributes of the project and the user's role in the project, avoiding the extraction of irrelevant project details, making the project entity set more representative, and complementing responsibility keywords and skill entities.

[0031] Step S26 merges and deduplicates the previously obtained sets of company / organization entities, skill entities, project entities, and job responsibility keywords to form an initial set of capability elements. During the merging process, a set merging algorithm integrates the entity information from the four dimensions into a unified set. Simultaneously, entity similarity calculation (using a cosine similarity algorithm with a similarity threshold of 0.8; entities with semantic similarity exceeding this threshold are considered duplicates) removes duplicates or highly similar entities. For example, "data analysis" and "data processing analysis" are semantically highly similar, so only one is retained. The physical significance of this step lies in integrating capability-related entity information from different dimensions to form a comprehensive and non-redundant initial set of capability elements, avoiding deviations in subsequent analysis results due to information dispersion or duplication. Technically, this is achieved by setting reasonable similarity thresholds and deduplication rules to reduce data redundancy while retaining core information. For example, it ensures that there are no duplicate records among company / organization entities, skill entities, project entities, and job responsibility keywords, enabling the initial set of capability elements to comprehensively and concisely cover key information related to the user's professional capabilities, laying a solid data foundation for subsequent clustering analysis.

[0032] Step S27 uses a clustering algorithm to divide the initial set of capability elements into multiple capability clusters and obtains the semantic center vector of each cluster. This step employs the K-means clustering algorithm. First, the optimal number of clusters K is determined according to the elbow rule (by calculating the sum of squares within each cluster for different K values; the optimal K value is the one where the rate of decrease in the sum of squares within each cluster slows down significantly; typically, K ranges from 3 to 8). Then, each entity in the initial set of capability elements is converted into a semantic vector (using the Word2Vec model for vector mapping, with a model dimension of 100 and a window size of 5). Finally, the K-means algorithm groups entities with similar semantic vectors into one class, forming multiple capability clusters. Each capability cluster represents a relatively concentrated capability direction; for example, a capability cluster containing entities such as "Python programming," "data analysis," "data visualization," and "algorithm iteration" can be classified as the "data technology application capability cluster." Subsequently, the average of the semantic vectors of all entities in each capability cluster is calculated to obtain the semantic center vector of that capability cluster. The semantic center vector represents the core semantic features of the capability cluster. For example, the semantic center vector of the "data technology application capability cluster" will be biased towards semantic directions related to data processing and algorithm application. The physical significance of this step is to classify the scattered capability elements according to semantic relevance, forming capability clusters with clear capability orientations. This makes the originally scattered entity information systematic and logical. Its technical contribution lies in the application of the K-means clustering algorithm, combined with reasonable parameter settings (such as optimal K value, vector dimension, etc.), which realizes the effective aggregation of capability elements, avoids the subjectivity of capability classification in traditional methods, and makes the division of capability clusters more objective and scientific. At the same time, the calculation of the semantic center vector provides a quantifiable semantic basis for subsequent time series screening.

[0033] Step S28 is a crucial step in generating core competency domain labels. It obtains nearest semantic center vectors through time series filtering, thereby acquiring core competency domain labels. First, it acquires the time information associated with the original entities in each competency cluster. This time information comes from work experience, including tenure, project execution time, and skill application time. For example, the time information for the skill entity "Python programming" is "2022-2023," and the time information for the skill entity "cross-departmental coordination" is "2020-2023." This time information is converted into corresponding timestamps (Unix timestamps in seconds) to construct the time series for each competency cluster. Then, based on the time series, multiple semantic center vectors are filtered according to the principle of being closest to the current time. Specifically, the average of the timestamps of all entities in each competency cluster is calculated, and this average is used as the time feature of the competency cluster. The closer the time feature is to the current timestamp, the closer the semantic center vector of the competency cluster is to the nearest semantic center vector. Finally, based on the semantic features of the nearest semantic center vectors and combined with a pre-defined capability domain label dictionary, core capability domain labels are generated. For example, if the nearest semantic center vectors are biased towards data technology application-related semantics, then the corresponding core capability domain label is "data technology application". The physical significance of this step is to filter out the user's most core and active capability direction at the current stage, ensuring that the core capability domain labels can reflect the user's latest capability status. Its technical contribution lies in fully considering the time attributes of capability elements, avoiding the problem of traditional methods that judge capabilities based solely on the entity itself while ignoring the time dimension. Through time series filtering, the core capability domain labels are made more timely and targeted. For example, for a user who possesses both the capability clusters of "data technology application" and "traditional project management", if the time characteristics of the "data technology application" capability cluster are closer to the current time, then it is given priority as the core capability domain label, which is more in line with the user's current capability development status.

[0034] In one embodiment, step S3, obtaining the static capability vector based on the core capability domain label and the professional behavior information, includes: S31. Obtain multiple core competency assessment scores of the user within a preset time period based on the core competency domain tags, and calculate core competency score feature values ​​based on the multiple core competency assessment scores. S32. Obtain professional behavior characteristic items based on the professional behavior information, wherein the professional behavior characteristic items include the frequency of professional practice and the duration of professional practice. S33. Obtain the practice stability coefficient based on the practice frequency and the practice time span; S34. The core competency score feature value and the professional stability coefficient are concatenated according to a preset time series to obtain a professional competency vector, and the professional competency vector is used as a static competency vector.

[0035] As described in steps S31-S34 above, the core of step S31 is to obtain multiple core competency assessment scores from the user within a preset time period and calculate the core competency score feature values. The data sources for the core competency assessment scores mainly include the company's internal performance appraisal system, evaluation results from third-party vocational skills assessment platforms, and specific scores after project completion. These scores correspond one-to-one with the core competency domain labels determined in step S2, ensuring the relevance of the scores. For example, if the core competency domain label is "data technology application," the obtained assessment scores should revolve around data processing, algorithm application, project data support, and other related competency dimensions. When calculating the core competency score feature values, a weighted average algorithm is used. The weights are related to the time distance of the scores, with recent scores having a higher weight than older scores to reflect the timeliness of the competency. The preset weighting rules are: scores from the last 3 months have a weight of 0.4, scores from 3-6 months have a weight of 0.3, scores from 6-12 months have a weight of 0.2, and scores from over 1 year have a weight of 0.1, with a total weight of 1. Assuming a user's core competency assessment scores over a preset period are: 90 points for the past 3 months, 85 points for 3-6 months, 88 points for 6-12 months, and 82 points for over 1 year, then the core competency score characteristic value = 90 × 0.4 + 85 × 0.3 + 88 × 0.2 + 82 × 0.1 = 36 + 25.5 + 17.6 + 8.2 = 87.3 points. The physical significance of this step lies in integrating the scattered core competency assessment scores from multiple time periods into a comprehensive quantitative indicator, objectively reflecting the user's current overall level in the core competency domain. Its technical implementation, through reasonable weighting, highlights the importance of recent competency performance, avoids the randomness of scores at a single point in time, and makes the core competency score characteristic value more representative and reliable, providing a quantitative basis for the core competency level in the static competency vector.

[0036] Step S32 aims to extract professional behavior characteristics from professional behavior information, including frequency of practice and duration of practice. Professional behavior information originates from user career records, work task management system data, and enterprise human resources attendance systems, etc. Frequency of practice refers to the number of times a user participates in core competency-related professional activities within a preset time period, such as the number of times they participate in data technology-related projects or complete data processing-related tasks. Duration of practice refers to the continuous length of time a user has worked in a current core competency-related position. If a user has multiple experiences in core competency-related positions, the sum of the durations of each period is taken. For example, if a user participates in data technology-related projects 5 times and completes data processing tasks 8 times within a preset 1-year time period, their frequency of practice is 13 times; if the user has worked continuously in a data technology-related position for 2 years and 3 months, their duration of practice is 2.25 years. The physical significance of this step lies in uncovering key features that reflect a user's professional activity and job stability, transforming abstract professional behavior into quantifiable and specific indicators. Its technical implementation ensures the objectivity and relevance of the features by clearly defining the characteristics and data extraction sources, avoiding quantitative bias caused by ambiguity of the features, and providing accurate basic data for subsequent calculation of the professional stability coefficient.

[0037] Step S33 calculates the employment stability coefficient based on the practice frequency and duration extracted in S32. This step uses a preset formula: Employment stability coefficient = (Practice duration / Preset time threshold) × 0.6 + ×0.4, where the preset time threshold is set according to the industry average tenure, for example, the preset time threshold in the data technology field is 2 years, and the frequency benchmark value is set according to the industry average frequency of professional activities, for example, the frequency benchmark value in the data technology field is 10 times. In the formula, the ratio of the professional time span to the preset time threshold reflects the user's continuous stability in the position, with a value range of [0,1]. The professional frequency is normalized by an exponential function, and the result reflects the user's professional activity, with a value range of [0,1]. The physical significance of this step is to integrate the information of the two dimensions of professional time span and professional activity into a comprehensive quantitative indicator, which fully reflects the stability of the user's professional behavior. Its technical contribution is that through reasonable formula design and parameter setting, it achieves the effective integration of two different dimension features, avoiding the one-sidedness of single feature evaluation. For example, considering only the professional time span may ignore the user's actual professional activity, and considering only the professional frequency may ignore the stability of the position. The professional stability coefficient combines the advantages of both and provides a quantitative basis for the stability of professional behavior for static ability vectors.

[0038] Step S34 concatenates the core competency score feature values ​​and the professional stability coefficient according to a preset time series to obtain a professional competency vector, which is then used as the static competency vector. The preset time series is set according to the actual application scenario, for example, according to the time segmentation order of "last 3 months - 3-6 months - 6-12 months - more than 1 year". The core competency score feature values ​​corresponding to each time period (if there is no score for a certain time period, 80% of the score of the previous time period is taken) are concatenated with the overall professional stability coefficient in an orderly manner. Using the above example, the user's core competency score feature values ​​for each time period are 90 points for the last 3 months, 85 points for 3-6 months, 88 points for 6-12 months, and 82 points for more than 1 year. The overall professional stability coefficient is 0.966. Then the concatenated static competency vector is [90, 85, 88, 82, 0.966] (the vector dimension can be adjusted according to the number of preset time segments). The physical significance of this step lies in integrating multi-time-period quantitative indicators of core competency levels with comprehensive indicators of professional behavior stability into a structured vector data, forming a quantitative representation that can comprehensively reflect a user's static professional competency. Its technical implementation, through a pre-set time series splicing method, not only preserves the core competency performance in different time periods but also integrates the professional stability coefficient, making the static competency vector more dimensional and comprehensive in information. This avoids the limitations of traditional single indicator vectors and lays a good data structure foundation for subsequent integration with dynamic competency evolution vectors.

[0039] In one embodiment, step S4, which involves obtaining a dynamic capability evolution vector based on the behavior sequence and the behavior quantification index, and constructing a dynamic profile of professional capabilities based on the dynamic capability evolution vector and the static capability vector, includes: S41. Obtain multiple behavioral events and their corresponding timestamps according to the behavioral sequence, and obtain multiple evolution intervals between the behavioral events and their adjacent behavioral events according to the multiple behavioral events and the multiple timestamps; S42. Obtain multiple behavioral quantification sub-indices corresponding to each behavioral event based on the behavioral quantification index, and calculate the comprehensive quantification value of each behavioral event based on the multiple behavioral quantification sub-indices. S43. Calculate behavioral evolution feature values ​​within multiple time windows based on multiple behavioral temporal features, multiple evolution intervals, and multiple comprehensive quantization values ​​in sequence; S44. Perform time-series regression analysis on multiple behavioral evolution characteristic values ​​to obtain behavioral trend indicators and behavioral fluctuation coefficients; S45. Obtain the baseline behavioral evolution feature value of the user in the same historical time period, and calculate the behavioral evolution difference value based on the behavioral evolution feature value and the baseline behavioral evolution feature value; S46. Vectorize and concatenate the behavioral trend index, the behavioral fluctuation coefficient and the behavioral evolution difference value to obtain the dynamic capability evolution vector. S47. The dynamic capability evolution vector and the static capability vector are weighted, fused, and normalized to form the dynamic profile of professional capabilities.

[0040] As described in steps S41-S47 above, the core of step S41 of this invention is to extract multiple behavioral events and their corresponding timestamps from the behavioral sequence, and to calculate the evolution interval between adjacent behavioral events. This data includes user skill learning records on professionally related platforms, task completion records during project execution, and ability demonstration records during work communication. The behavioral sequence is a collection of these dynamic behaviors arranged in chronological order. Using timestamp extraction technology, each behavioral event is assigned a corresponding timestamp (Unix timestamp in seconds). For example, the timestamp corresponding to "completing an advanced Python course" is 1672502400 (January 1, 2023, 00:00:00), and the timestamp corresponding to "participating in an e-commerce data analysis project" is 1675276800 (February 1, 2023, 00:00:00). Subsequently, the difference between the timestamps of two adjacent behavioral events is calculated to obtain the evolution interval, i.e., 1675276800 - 1672502400 = 2774400 seconds (32 days). The physical significance of this step lies in decomposing a continuous sequence of behaviors into discrete behavioral events, and quantifying the temporal distribution characteristics of these events through timestamps and evolutionary intervals. This provides a foundation for subsequent analysis of the rhythm and patterns of behavioral evolution. The technical implementation ensures the accuracy of the evolutionary intervals through precise timestamp extraction and difference calculation. For example, it avoids calculation errors caused by inconsistent time formats, making the temporal correlation of behavioral events clear and quantifiable, and providing reliable temporal dimension data for subsequent calculation of evolutionary features.

[0041] Step S42, based on behavioral quantification indices, obtains multiple behavioral quantification sub-indices for each behavioral event and calculates a comprehensive quantitative value. Behavioral quantification indices are quantitative representations of dynamic behavioral data. Data sources include course assessment scores from skills learning platforms, task completion quality scores from project management systems, and peer assessment scores. Each behavioral event's behavioral quantification sub-indices specifically include completion quality index, engagement level index, and outcome impact index, with each index ranging from [0,1]. The comprehensive quantitative value is calculated using a weighted summation formula. Preset weights are set according to the behavioral type and the importance of the sub-indices. For example, in skills learning behaviors, the completion quality index has a weight of 0.5, the engagement level index has a weight of 0.3, and the outcome impact index has a weight of 0.2; in project participation behaviors, the completion quality index has a weight of 0.4, the engagement level index has a weight of 0.2, and the outcome impact index has a weight of 0.4. Suppose a user's behavioral event of "participating in an e-commerce data analysis project" has a completion quality index of 0.9, an engagement index of 0.8, and an impact index of 0.7. Then its comprehensive quantitative value = 0.9 × 0.4 + 0.8 × 0.2 + 0.7 × 0.4 = 0.8. The physical significance of this step lies in quantifying the multi-dimensional performance of each behavioral event into a comprehensive indicator, objectively reflecting the degree of contribution of this behavioral event to the user's ability evolution. Its technical implementation, through reasonable weighting, allows the comprehensive quantitative value to highlight the impact of core sub-indices. For example, project-related behaviors emphasize the impact of results, while skill-learning behaviors emphasize completion quality, avoiding the one-sidedness of single-dimensional evaluation and providing a quantitative basis for the subsequent calculation of behavioral evolution characteristic values ​​at the behavioral quality level.

[0042] Step S43 calculates behavioral evolution feature values ​​within multiple time-series windows based on multiple behavioral temporal features, evolution intervals, and comprehensive quantization values. First, behavioral temporal features include the type distribution and frequency of behavioral events, obtained by statistically analyzing the proportion of different types of behavioral events and the average interval between their occurrence within each time-series window. The time-series window is set based on the density of dynamic behavioral data, with a preset window duration of 30 days and a sliding step of 15 days, meaning each time-series window covers 30 days of behavioral data, with adjacent windows overlapping by 15 days. Within each time-series window, a feature fusion algorithm is used to fuse the behavioral temporal features (quantized into values ​​in the [0,1] interval), the evolution interval (after normalization, the formula is normalized evolution interval = evolution interval / window duration), and the comprehensive quantization value to calculate the behavioral evolution feature value. The fusion formula is: Behavioral evolution feature value = (Behavioral temporal feature × 0.3 + Normalized evolution interval × 0.2 + Comprehensive quantization value × 0.5). For example, within a certain time series window, if the behavioral time series feature value is 0.7, the normalized evolution interval is 0.3 (evolution interval 9 days, window duration 30 days), and the comprehensive quantification value is 0.8, then the behavioral evolution feature value for this window = 0.7 × 0.3 + 0.3 × 0.2 + 0.8 × 0.5 = 0.67. The physical significance of this step lies in integrating multi-dimensional behavioral information within the time series window into a comprehensive evolution feature value, achieving a quantitative representation of the behavioral evolution state within a specific time period. Its technical contribution lies in dividing the continuous behavioral evolution process into analyzable segments through the setting of the time series window, and simultaneously highlighting the core role of the comprehensive quantification value (behavioral quality) through reasonable weight allocation, enabling the behavioral evolution feature value to accurately reflect the actual state of user capability evolution within that time period, providing a segmented quantification basis for subsequent trend analysis.

[0043] Step S44 performs time-series regression analysis on behavioral evolution characteristic values ​​across multiple time windows to obtain behavioral trend indicators and behavioral volatility coefficients. This step employs a linear regression algorithm, using the time window number as the independent variable (e.g., 1 for the first window, 2 for the second, and so on) and the behavioral evolution characteristic value as the dependent variable to construct a linear regression model: y = ax + b, where y is the behavioral evolution characteristic value, x is the window number, a is the regression coefficient, and b is the intercept term. The behavioral trend indicator is the regression coefficient a. A positive a value indicates an upward trend in the behavioral evolution characteristic value over time, reflecting a continuous improvement in user capabilities; a negative a value indicates a downward trend; a value close to 0 indicates a stable trend; the larger the absolute value of a, the more pronounced the trend. The behavioral volatility coefficient is obtained by calculating the standard deviation between the behavioral evolution characteristic value and the predicted value of the regression model for each time window. A larger standard deviation indicates poorer stability and more drastic fluctuations in behavioral evolution. For example, after linear regression analysis, the regression coefficient a = 0.05 is obtained, and the standard deviation between the behavioral evolution characteristic values ​​and the predicted values ​​for each window is 0.08. Therefore, the behavioral trend index is 0.05, and the behavioral fluctuation coefficient is 0.08. The physical significance of this step lies in extracting the overall trend and fluctuation characteristics from the evolution characteristic values ​​of multiple time-series windows, quantifying the direction and stability of user capability evolution. Technically, this is achieved through the application of linear regression algorithms combined with standard deviation calculation, realizing a precise characterization of evolutionary trends and fluctuations. This avoids the subjective judgment of trends in traditional methods, making the behavioral trend index and behavioral fluctuation coefficient more objective and scientific, and providing core trend and fluctuation dimension information for the dynamic capability evolution vector.

[0044] Step S45 obtains the user's baseline behavioral evolution characteristic values ​​for the same historical period and calculates the behavioral evolution difference value. The baseline behavioral evolution characteristic values ​​are derived from a historical database. They are calculated by retrieving the user's dynamic behavioral data for the same period of the previous year (e.g., January-March of this year corresponds to January-March of last year) using the same method as steps S41 to S43, ensuring the comparability of the baseline data. The behavioral evolution difference value is calculated as the difference between the average behavioral evolution characteristic value for the current period and the average baseline behavioral evolution characteristic value. The formula is: Behavioral Evolution Difference Value = Average Characteristic Value for the Current Period - Average Characteristic Value for the Baseline Period. A positive difference value indicates that the user's current ability evolution is better than the same period in history; a negative value indicates it is worse than the same period in history. For example, if the average behavioral evolution characteristic value for the user's current period (January-March of this year) is 0.75, and the average baseline behavioral evolution characteristic value for the same historical period (January-March of last year) is 0.68, then the behavioral evolution difference value = 0.75 - 0.68 = 0.07. The physical significance of this step lies in quantifying the relative level of a user's current ability evolution by comparing it with the same historical period, reflecting the speed and magnitude of the user's ability growth. Its technical contribution lies in introducing historical benchmark data, avoiding the limitations of only focusing on the current evolutionary state, and making the characterization of user ability evolution more in-depth and valuable. For example, the difference value can be used to determine whether the user is making continuous progress and whether the magnitude of progress is significant, supplementing the dynamic ability evolution vector with information on the relative difference dimension.

[0045] Step S46 vectorizes and concatenates the behavioral trend indicator, behavioral fluctuation coefficient, and behavioral evolution difference value to obtain a dynamic capability evolution vector. The vectorization concatenation uses an ordered concatenation method, integrating the values ​​of the three indicators into a three-dimensional vector in the order of "behavioral trend indicator - behavioral fluctuation coefficient - behavioral evolution difference value." Using the example above, with a behavioral trend indicator of 0.05, a behavioral fluctuation coefficient of 0.08, and a behavioral evolution difference value of 0.07, the dynamic capability evolution vector is [0.05, 0.08, 0.07]. If the indicator dimensions are subsequently expanded according to actual needs, vector dimensions can be added using the same logic. The physical significance of this step lies in integrating the three core indicators reflecting capability evolution trends, fluctuations, and relative differences into a structured vector data, achieving a centralized representation of the dynamic evolution characteristics of user capabilities. Its technical implementation, through ordered concatenation, ensures that the meaning of each dimension of the vector is clear and interpretable, avoiding subsequent calculation errors caused by indicator confusion. This allows the dynamic capability evolution vector to be directly used for fusion with the static capability vector, providing core data for the construction of a dynamic professional capability profile.

[0046] Step S47 involves weighted fusion and normalization of the dynamic capability evolution vector and the static capability vector to construct a dynamic profile of professional capabilities. The weights for weighted fusion are set according to the application scenario and job type. The default general weights are: dynamic capability evolution vector weight 0.4, static capability vector weight 0.6; for growth-oriented positions, the dynamic capability evolution vector weight can be adjusted to 0.5-0.6, and the static capability vector weight to 0.4-0.5; for stable positions, the dynamic capability evolution vector weight can be adjusted to 0.3-0.4, and the static capability vector weight to 0.6-0.7. The fusion formula is: Fusion Vector = Dynamic Capability Evolution Vector × Dynamic Weight + Static Capability Vector × Static Weight. Subsequently, the Min-Max normalization method is used to map the values ​​of each dimension of the fusion vector to the [0,1] interval. The normalization formula is: Normalized Value = (Original Value - Minimum Value) / (Maximum Value - Minimum Value), where the minimum and maximum values ​​are the historical extreme values ​​of all user data for that dimension. For example, given a dynamic ability evolution vector [0.05, 0.08, 0.07] and a static ability vector [90, 85, 88, 82, 0.966], under general weights, the first three dimensions of the fused vector are 0.05 × 0.4 + the corresponding static vector dimension (assuming the first three dimensions of the static vector are core ability scores) × 0.6, and the last two dimensions are the last two dimensions of the static vector × 0.6 + the supplementary dynamic vector dimension (if any) × 0.4. After calculation and normalization, the final dynamic profile vector of professional ability is obtained. The physical significance of this step lies in organically integrating static information reflecting the user's current ability status with dynamic information reflecting the ability evolution trend to form a comprehensive and three-dimensional dynamic profile of professional ability. Its technical contribution lies in the flexible weight adjustment mechanism, which enables the profile to adapt to the needs of different types of positions. At the same time, the normalization process ensures the comparability of each dimension of the vector, avoiding fusion deviations caused by differences in numerical ranges. This allows the dynamic profile of professional ability to reflect both the user's current stable ability and their future growth potential, providing a more comprehensive and accurate basis for subsequent job matching.

[0047] In one embodiment, step S5, which involves obtaining a job competency requirement semantic vector and a job context feature value based on the job requirement data, and constructing a job profile based on the job competency requirement semantic vector and the job context feature value, includes: S51. Perform structured parsing on the job requirement data to obtain the job description text segment, job requirement text segment, and organizational environment text segment; S52. Perform semantic analysis on the job description text segment and the job requirement text segment, extract multiple capability keyword entities, and map the capability keyword entities to the corresponding capability dimensions according to the preset capability classification system to form an initial set of job capability requirements; S53. Extract multiple organizational feature entities based on the organizational environment text segment. The organizational feature entities include industry type, team size and business maturity tags, which constitute an initial set of job context feature values. S54. Obtain the entity weights for each capability dimension in the initial set of job capability requirements, wherein the entity weights are calculated based on the frequency of the entity's occurrence in the text and a preset importance coefficient. S55. Perform feature quantization on each organizational feature entity in the initial job context feature value set to obtain the corresponding job context feature value; S56. Obtain a preset initial value for each entity weight, calculate multiple capability requirement intensity values ​​based on multiple preset initial values ​​and multiple entity weights, and merge the multiple capability requirement intensity values ​​to generate a job capability requirement semantic vector. S57. The job context feature value and the job competency requirement semantic vector are concatenated and input into a preset job profile generation model for feature fusion and dimensionality reduction, and a structured job profile vector is output.

[0048] As described in steps S51-S57 above, step S51 serves as the starting point for building the job profile. It aims to perform structured parsing of the job requirement data, separating it into responsibility description text segments, job requirement text segments, and organizational environment text segments. The job requirement data originates from enterprise recruitment information platforms, job descriptions in human resource management systems, etc., and this data is typically a comprehensive text containing various types of information. This step employs a text structure parsing algorithm, combined with preset text segmentation rules (based on keywords such as "responsibility," "requirements," "environment," "team," and "industry" to trigger segmentation), splitting the complete job requirement text into three core parts according to semantic categories. The job description section mainly covers the work tasks, core responsibilities, and work objectives required for the position, such as "responsible for market research, requirements analysis, and feature planning for the product, and driving product iteration and upgrades"; the job requirements section clarifies the requirements for the job applicant's professional skills, educational background, work experience, and comprehensive qualities, such as "bachelor's degree or above, more than 3 years of experience as an internet product manager, and proficiency in product prototyping tools"; the organizational environment section describes the environmental characteristics such as the industry type, team size, business maturity, and corporate culture of the position, such as "internet industry, team size of 20-50 people, business in the growth stage, and a corporate culture that advocates innovation and collaboration." The physical significance of this step lies in breaking down the holistic and unstructured nature of the job requirements text, classifying the scattered information according to semantic function, and laying the foundation for subsequent targeted feature extraction. The technical implementation is achieved through preset segmentation rules and keyword matching algorithms, ensuring that the boundaries of each segmented text are clear and the content is focused, avoiding the cross-mixing of different types of information. For example, "communication and coordination ability" in "possessing good communication and coordination skills and a harmonious team atmosphere" is classified into the job requirements text segment, while "harmonious team atmosphere" is classified into the organizational environment text segment, improving the targeting and efficiency of subsequent information extraction.

[0049] Step S52 performs semantic analysis on the job description and job requirements text segments, extracting key competency entities and mapping them to corresponding competency dimensions to form an initial set of job competency requirements. This step first uses a keyword entity extraction algorithm based on a BERT pre-trained model to perform semantic analysis on the two text segments, identifying and extracting competency-related key competencies. These entities include professional technical skills (such as "product prototype design" and "data analysis"), general skills (such as "communication and coordination" and "project management"), work experience (such as "more than 3 years of product manager experience"), and educational background (such as "bachelor's degree or above"). Subsequently, according to a pre-defined competency classification system (built on industry-standard competency classification criteria, divided into three main categories: professional technical skills, general skills, and qualification requirements, with each category further subdivided into specific dimensions, such as design skills, analytical skills, and programming skills under professional technical skills), the extracted key competency entities are mapped one by one to their corresponding competency dimensions. For example, "product prototype design" maps to the "professional technical skills - design skills" dimension, "communication and coordination" maps to the "general skills - interpersonal communication skills" dimension, and "more than 3 years of product manager experience" maps to the "qualification requirements - work experience" dimension. These mapped capability keyword entities together constitute the initial set of job capability requirements. The physical significance of this step lies in transforming unstructured text descriptions into structured capability requirement information, clarifying the specific dimensions of talent capabilities that the job requires. Its technical contribution lies in improving the accuracy of keyword entity extraction through the application of the BERT model, especially the ability to identify ambiguous text. For example, it can accurately distinguish whether "design" refers to "product design" or "graphic design" in different contexts. At the same time, the pre-set capability classification system ensures the standardization and consistency of capability dimension mapping, avoids the subjectivity of capability classification, and makes the initial set of job capability requirements more systematic and operable.

[0050] Step S53 extracts organizational feature entities from the organizational environment text segment to form an initial set of job context feature values. This step uses an organizational feature entity recognition algorithm, combined with a pre-set organizational feature dictionary (covering relevant terms such as industry type, team size, business maturity, and corporate culture), to extract key feature entities from the organizational environment text segment. These include industry type entities such as "Internet," "Finance," and "Manufacturing"; team size entities such as "20-50 people" and "more than 50 people"; business maturity tags such as "startup," "growth stage," and "mature stage"; and corporate culture entities such as "innovation and collaboration" and "rigorous and standardized." These extracted organizational feature entities directly reflect the job's work environment and organizational attributes, and are indispensable contextual information in the job profile. Entities such as "Internet industry," "growth stage business," and "20-50 person team" collectively constitute the initial set of job context feature values. The physical significance of this step lies in uncovering the organizational environment adaptation requirements behind job requirements, transforming abstract environmental descriptions into concrete feature entities. Its technical implementation combines organizational feature dictionaries with semantic similarity calculations, ensuring the comprehensiveness and accuracy of feature entity extraction. For example, the business maturity label "growth stage" is extracted from "the company is in a rapid development stage and its business is expanding rapidly," and the corporate culture entity "innovative and active" is extracted from "the team members are mainly young people and the work atmosphere is active." This provides rich basic data for the subsequent quantification of job context feature values.

[0051] Step S54 calculates the entity weight for each capability dimension in the initial set of job capability requirements. This weight is calculated based on the entity's frequency of occurrence in the text and a preset importance coefficient. First, the frequency of each capability keyword entity is counted in the job description and job requirements text sections. A higher frequency indicates greater importance of the entity in the job capability requirements. For example, if "product prototype design" appears 3 times and "data analysis" appears 2 times, the former has a higher frequency than the latter. Second, the importance coefficients for each capability dimension are preset according to the job type. For example, for a product manager position, the importance coefficient for the "professional technical skills" dimension is set to 0.5, for the "general skills" dimension to 0.3, and for the "experience requirements" dimension to 0.2; for a technical development position, the importance coefficients for the "professional technical skills" dimension are set to 0.6, for the "general skills" dimension to 0.2, and for the "experience requirements" dimension to 0.2. The formula for calculating the entity weight is: Entity weight = (Frequency of this entity / Total frequency of all entities under the same capability dimension) × Importance coefficient of this capability dimension. For example, under the "Professional Technical Abilities" dimension of the product manager position, "Product Prototype Design" appears 3 times and "Data Analysis" appears 2 times, for a total frequency of 5. The importance coefficient for this dimension is 0.5. Therefore, the entity weight of "Product Prototype Design" is (3 / 5) × 0.5 = 0.3, and the entity weight of "Data Analysis" is (2 / 5) × 0.5 = 0.2. The physical significance of this step lies in quantifying the relative importance of different ability keywords in the job requirements, avoiding treating all ability requirements the same. Its technical contribution lies in combining frequency of occurrence with importance coefficients, considering both the objective representation in the text description and the subjective needs of the job type. This makes the calculation of entity weights more targeted and reasonable. For example, technical positions emphasize professional technical abilities, thus assigning them a higher importance coefficient, ensuring that the weight allocation matches the actual needs of the position, and providing a precise weight basis for subsequently generating the semantic vector of job ability requirements.

[0052] Step S55 quantifies the features of each organizational feature entity in the initial job context feature value set to obtain the corresponding job context feature value. Different organizational characteristics are quantified using different methods: For industry-type entities, one-hot encoding is used, for example, the internet industry is coded as [1,0,0], the financial industry as [0,1,0], and the manufacturing industry as [0,0,1]. For team-size entities, values ​​are mapped to intervals, for example, less than 10 people are mapped to 1, 10-50 people to 2, 50-100 people to 3, and more than 100 people to 4. For business maturity tags, values ​​are mapped to development stages as [0.3,0.6,0.9], i.e., 0.3 for the startup stage, 0.6 for the growth stage, and 0.9 for the mature stage. For corporate culture entities, values ​​are mapped to the interval [0,1] based on their relevance to job positions, combined with preset corporate culture quantification standards. For example, "innovation and collaboration" has a high relevance to product manager positions and is quantified as 0.8, while "rigorous and standardized" has a high relevance to finance positions and is quantified as 0.9. These quantification methods transform non-numerical organizational features into calculable job contextual feature values. The physical significance of this step lies in achieving a quantitative representation of organizational environment characteristics, enabling the integration of job contextual information into subsequent job profile vectors. Technically, this is achieved by designing reasonable quantification schemes for different types of feature entities, ensuring that the quantification results accurately reflect the attributes of the features themselves while possessing good comparability and calculability. For example, the quantified value of team size can intuitively reflect differences in team size, and the quantified value of business maturity can reflect the stage characteristics of business development, providing a quantitative contextual basis for the subsequent construction of job profile vectors.

[0053] Step S56 obtains the preset initial value for each entity weight, calculates the capability requirement intensity value based on the entity weight, and then generates a semantic vector of job capability requirements. The preset initial value for entity weight is set according to the industry-standard capability requirement intensity. Each capability dimension corresponds to a preset initial value. For example, the preset initial value for the "Professional Technical Capability" dimension is 10, for the "General Capability" dimension it is 8, and for the "Qualification Requirements" dimension it is 6. The formula for calculating the capability requirement intensity value is: Capability Requirement Intensity Value = Preset Initial Value × Entity Weight × 10 (multiplying by 10 is to amplify the value to a suitable range for easier subsequent calculations). For example, the entity weight for "Product Prototype Design" is 0.3, and the corresponding preset initial value for the "Professional Technical Capability" dimension is 10, so its capability requirement intensity value = 10 × 0.3 × 10 = 30; the entity weight for "Communication and Coordination" is 0.2, and the corresponding preset initial value for the "General Capability" dimension is 8, so its capability requirement intensity value = 8 × 0.2 × 10 = 16. Subsequently, the capability requirement intensity values ​​of all capability keyword entities are merged according to a preset order (i.e., the dimensional order of the capability classification system) to form a one-dimensional vector, which is the job capability requirement semantic vector. For example, the job capability requirement semantic vector for the product manager position is [30, 20, 16, 12, ...], where each value corresponds to the capability requirement intensity value of different capability keyword entities. The physical significance of this step lies in further quantifying the structured capability requirement information into computable vector data, intuitively reflecting the required intensity of each capability for the position. Its technical contribution lies in making the calculation of capability requirement intensity values ​​more industry-relevant by introducing preset initial values, avoiding the problem of an excessively small or unreasonable range of intensity values ​​caused by relying solely on entity weights. At the same time, the fixed-order merging method ensures the structure and consistency of the job capability requirement semantic vector, providing a unified data format for subsequent matching calculations with the dynamic profile vector of professional capabilities.

[0054] Step S57 concatenates the job context feature values ​​with the job competency requirement semantic vector, inputs it into the preset job profile generation model for feature fusion and dimensionality reduction, and outputs a structured job profile vector. First, the job context feature values ​​obtained in S55 (which have been quantized into vector form, such as industry type independent encoding vector, team size quantification value, business maturity quantification value, etc.) are concatenated with the job competency requirement semantic vector generated in S56 in an ordered manner to form a high-dimensional comprehensive feature vector. For example, if the job context feature vector is [1,0,0,2,0.6,0.8] and the job competency requirement semantic vector is [30,20,16,12], the concatenated comprehensive feature vector is [1,0,0,2,0.6,0.8,30,20,16,12]. Subsequently, the comprehensive feature vector is input into a pre-defined job profile generation model. This model is based on a neural network and includes an input layer, hidden layers, and an output layer. The number of neurons in the input layer matches the dimension of the comprehensive feature vector. Two hidden layers are used: the first layer has 64 neurons using the ReLU activation function, and the second layer has 32 neurons, also using the ReLU activation function. The number of neurons in the output layer is set to 16 (i.e., the dimension of the final job profile vector), and the Sigmoid activation function maps the output values ​​to the [0,1] interval. During model training, mean squared error is used as the loss function, the Adam optimizer is selected, the learning rate is set to 0.001, and the number of iterations is set to 200. Through training, the model learns the inherent correlation of job features, achieving effective feature fusion and dimensionality reduction. Finally, the 16-dimensional vector output by the model is the structured job profile vector. This vector integrates the strength of the job's competency requirements and contextual features, comprehensively and accurately representing the core requirements of the job. The physical significance of this step lies in transforming high-dimensional, scattered job feature data into low-dimensional, compact structured vectors, thereby achieving a quantitative representation of job profiles. Its technical contribution lies in the application of neural network models, which enables deep integration of competency requirements and contextual features, avoiding feature redundancy caused by simple splicing. At the same time, dimensionality reduction reduces the computational power requirements for subsequent matching calculations, improving matching efficiency. Furthermore, the model's parameter settings (such as the number of hidden layer neurons, learning rate, and number of iterations) have been optimized for the job profile construction scenario, ensuring that the model can accurately capture key information of job features, so that the output job profile vector has good discriminative and representational capabilities.

[0055] In one embodiment, step S6, which involves matching the dynamic profile of professional skills with the job profile to obtain job matching information and recommending jobs to the user based on the job matching information, includes: S61. Obtain the corresponding dynamic profile vector of professional ability based on the dynamic profile of professional ability; S62. Obtain the corresponding job requirement vector based on the job profile; S63. Calculate the vector similarity between the dynamic profile vector of professional competence and the vector of job requirements based on cosine similarity to obtain a competence matching score; S64. Extract the behavioral trend indicators and behavioral fluctuation coefficients contained in the dynamic profile of professional competence; S65. Obtain the preset growth adaptability weights in the job profile, and calculate the growth trend matching degree based on the growth adaptability weights, the behavioral trend indicators, and the behavioral fluctuation coefficients. S66. Normalize the ability matching score and the growth trend matching score to obtain the normalized value of the ability matching score and the normalized value of the growth trend matching score. Then, weight and fuse the normalized value of the ability matching score and the normalized value of the growth trend matching score to generate the comprehensive job matching score. S67. Obtain multiple candidate positions based on the job profile, sort the multiple candidate positions according to the overall job matching degree, and obtain a job recommendation list; S68. Recommend jobs to users based on the job recommendation list.

[0056] As described in steps S61-S68 above, the core of this step is to extract the corresponding dynamic profile vector of professional competence from the dynamic profile. The dynamic profile of professional competence is a structured vector obtained after weighted fusion and normalization processing in step S47 above. Its data comes directly from the output of this step, and the vector contains multi-dimensional information such as user static competence features (e.g., core competence score feature value, job stability coefficient) and dynamic evolution features (e.g., behavioral trend index, behavioral fluctuation coefficient, behavioral evolution difference value). For example, a user's dynamic profile vector of professional competence is [0.87, 0.96, 0.05, 0.08, 0.07], where each dimension corresponds to the normalized value of the core competence score, the normalized value of the job stability coefficient, the behavioral trend index, the behavioral fluctuation coefficient, and the behavioral evolution difference value, respectively. The physical significance of this step lies in transforming the abstract dynamic profile of professional competence into calculable and comparable vector data, providing a standardized numerical basis for subsequent matching with job requirement vectors. The technical implementation directly uses the profile vectors constructed earlier, ensuring the continuity and consistency of the data, avoiding information distortion caused by secondary processing, and enabling the vectors to fully retain the core characteristics of the user's competence, providing reliable data support for accurate matching.

[0057] Step S62 extracts the corresponding job requirement vector from the job profile. This vector is the structured job profile vector output by the job profile generation model in step S57. The data comes from the model output of this step. The vector integrates the semantic features of job competency requirements and the feature values ​​of job context. For example, the job requirement vector for a product manager position is [0.82, 0.75, 0.68, 0.90, 0.72], with each dimension corresponding to the intensity of professional technical ability requirements, the intensity of general ability requirements, the intensity of seniority requirements, team size fit, and business maturity fit, respectively. The physical significance of this step is to transform the core features of the job profile into a vector form with the same dimension and comparable to the dynamic professional competency profile vector, thus building a unified numerical framework for subsequent similarity calculations. Its technical implementation is achieved by directly extracting the standardized vector output by the model, ensuring the integrity and standardization of the job requirement vector. The meaning of each dimension of the vector corresponds to the dynamic professional competency profile vector. For example, the dimension of the intensity of the job's professional technical ability requirements corresponds to the dimension of the user's core competency rating, laying the foundation for accurate calculation of competency matching.

[0058] Step S63 uses the cosine similarity algorithm to calculate the similarity between the dynamic profile vector of professional abilities and the vector of job requirements, thus obtaining an ability matching score. The core of the cosine similarity algorithm is to measure the degree of similarity between two vectors by calculating the cosine value of the angle between them. The formula is: cosθ=(A·B) / (||A||×||B||), where A is the dynamic profile vector of professional abilities, B is the vector of job requirements, A·B is the dot product of the two vectors, and ||A|| and ||B|| are the magnitudes of the two vectors, respectively. The algorithm's value range is [-1, 1]. The closer the cosine value is to 1, the higher the similarity between the two vectors, and the stronger the match between the user's current abilities and the job requirements; the closer it is to -1, the lower the similarity and the weaker the match. For example, a user's dynamic profile vector A for professional skills is [0.87, 0.96, 0.05, 0.08, 0.07], and the vector B for job requirements is [0.82, 0.75, 0.68, 0.90, 0.72]. The dot product A·B = 0.87 × 0.82 + 0.96 × 0.75 + 0.05 × 0.68 + 0.08 × 0.90 + 0.07 × 0.72 ≈ 1.5898; the magnitude of vector A is ||A|| = ≈1.301; The magnitude of vector B is ||B||= ≈√(0.6724+0.5625+0.4624+0.81+0.5184)≈1.739; therefore, the cosine similarity is... ≈1.5898 / (1.301×1.739)≈1.5898 / 2.262≈0.7, meaning the ability matching score is 0.7. The physical significance of this step lies in quantifying the degree of fit between the user's current professional abilities and the job requirements. Its technical contribution lies in the fact that the cosine similarity algorithm is sensitive to the relative relationships between vector dimensions, effectively capturing the compatibility between various dimensions of the user's abilities and various dimensions of the job requirements. Compared to simple Euclidean distance calculation, it is more suitable for measuring the similarity of high-dimensional vectors, and the algorithm has low computational complexity and high efficiency, quickly obtaining accurate ability matching results, providing a core basis for calculating the overall job matching degree.

[0059] Step S64 extracts behavioral trend indicators and behavioral fluctuation coefficients from the dynamic profile of professional competence. These two indicators are the core dynamic evolutionary features obtained from time-series regression analysis in step S44, and the data comes directly from the calculation results of this step. The behavioral trend indicator reflects the overall direction of user competence evolution (rising, falling, or stable), while the behavioral fluctuation coefficient reflects the stability of user competence evolution (drastic fluctuations or stable performance). For example, a user's behavioral trend indicator is 0.05 (showing a moderate upward trend), and the behavioral fluctuation coefficient is 0.08 (small fluctuations, stable evolution). The physical significance of this step lies in extracting core dynamic features that reflect a user's growth potential, providing key data for subsequent calculations of growth trend matching. Its technical implementation directly calls the previously calculated indicator results, ensuring data accuracy and consistency, avoiding efficiency losses caused by repeated calculations. At the same time, these two indicators can intuitively reflect the user's growth status, providing a quantitative basis for measuring the suitability of the user to the job's growth requirements.

[0060] Step S65 retrieves the pre-set growth adaptability weights from the job profile and calculates the growth trend matching degree by combining behavioral trend indicators and behavioral fluctuation coefficients. The growth adaptability weights are pre-set parameters based on characteristics such as the job's growth potential and business development stage. The data comes from the company's positioning and requirements for the job during the job profile construction process. For example, for business positions with large growth potential and in a growth stage, the growth adaptability weight is set to 0.7; for positions with smaller growth potential and mature business, the growth adaptability weight is set to 0.3. This weight reflects the degree to which the job emphasizes the job seeker's growth potential; a higher weight indicates that the job places greater emphasis on the job seeker's growth trend and development potential. The formula for calculating the growth trend matching degree is: Growth Trend Matching Degree = Behavioral Trend Indicator × Growth Adaptability Weight + (1 - Behavioral Fluctuation Coefficient) × (1 - Growth Adaptability Weight), where (1 - Behavioral Fluctuation Coefficient) reflects the supporting role of the stability of the user's ability evolution on growth potential. The smaller the fluctuation, the stronger the stability, and the higher the predictability of growth potential. Using the above example, with a job growth adaptability weight of 0.7, a user behavior trend index of 0.05, and a behavior fluctuation coefficient of 0.08, the growth trend matching degree = 0.05 × 0.7 + (1 - 0.08) × (1 - 0.7) = 0.311. The physical significance of this step lies in quantifying the degree of fit between user growth trends and job growth requirements. Its technical contribution is that by introducing a growth adaptability weight, the matching calculation can adapt to the differences in requirements of different types of jobs. Simultaneously, by combining behavioral trends and fluctuation coefficients, it comprehensively considers the "direction" and "stability" of user growth potential, avoiding the one-sidedness of evaluating growth potential with a single indicator. For example, focusing only on an upward trend while ignoring the potential unsustainability caused by drastic fluctuations makes the calculation of the growth trend matching degree more scientific and targeted.

[0061] Step S66 normalizes the competency matching score and growth trend matching score, and then generates the overall job matching score through weighted fusion. First, the Min-Max normalization method is used to map the values ​​of the two indicators to the [0,1] interval. The normalization formula is: Normalized value = (Original value - Minimum indicator value) / (Maximum indicator value - Minimum indicator value), where the minimum and maximum indicator values ​​are derived from the extreme values ​​in historical matching data to ensure the comparability of the normalized data. For example, if the original competency matching score is 0.703, the historical minimum is 0.2, and the maximum is 0.95, then its normalized value = (0.703 - 0.2) / (0.95 - 0.2) ≈ 0.671; and the original growth trend matching score is 0.311, the historical minimum is 0.1, and the maximum is 0.8, then its normalized value = (0.311 - 0.1) / (0.8 - 0.1) ≈ 0.301. Subsequently, based on the priority of job recruitment, the weights of the two indicators are preset for integration. In the general scenario, the weight of the normalized value of ability matching is set to 0.6, and the weight of the normalized value of growth trend matching is set to 0.4. For positions in startups or growth-oriented businesses, the weights can be adjusted to 0.5 for both ability matching and growth trend matching. The weighted integration formula is: Overall Job Matching Degree = Normalized Value of Ability Matching × Ability Weight + Normalized Value of Growth Trend Matching × Growth Weight. Using the weight settings for the general scenario, the Overall Job Matching Degree = 0.671 × 0.6 + 0.301 × 0.4 ≈ 0.523. The physical significance of this step lies in integrating two different dimensions of matching indicators into a comprehensive quantitative value, which fully reflects the overall fit between users and positions. Its technical contribution is that the normalization process eliminates the differences in numerical ranges between different indicators, ensuring the fairness of the integration. The flexible integration weight setting allows the comprehensive matching degree to adapt to the recruitment preferences and job type requirements of different companies. For example, companies that focus on current capabilities can increase the weight of capability matching degree, while companies that focus on long-term development can increase the weight of growth trend matching degree, making the comprehensive job matching degree more practical and flexible.

[0062] Step S67 filters multiple candidate positions from the job database based on the job profile and sorts them by overall job match to obtain a recommended job list. The selection criteria for candidate positions are a preliminary match between the core features in the job profile and the user's basic needs (such as work location, salary range, etc.). The job database stores all job information posted by companies and their corresponding job profile vectors, with data sourced from the synchronized updates of the company's recruitment information entry and job profile building system. During the filtering process, job types explicitly rejected by the user, positions exceeding salary expectations, or positions with mismatched work locations are first excluded. Then, positions whose core competency requirements in the job profile match the user's core competency domain tags are retained, forming a candidate job set. Subsequently, the overall job match scores of each position in the candidate job set are sorted from highest to lowest. Positions with higher overall match scores are ranked higher in the recommended list. For example, if candidate job A has an overall match score of 0.82, job B has 0.75, and job C has 0.523, then the recommended list order would be A, B, C. The physical significance of this step lies in filtering out candidate positions from a massive pool of job postings that meet the user's basic needs and are relevant to the user's abilities and growth trends. By ranking these positions, the most suitable ones are highlighted. The technical contribution is that the initial screening process reduces the interference of invalid positions and improves recommendation efficiency. The ranking based on comprehensive matching ensures the accuracy of the recommendation results, allowing users to see the most suitable positions first, avoiding the problems of chaotic job ranking and high-quality positions being buried in traditional recommendations.

[0063] Step S68 recommends jobs to users based on the job recommendation list. This involves presenting the sorted candidate job information (including job title, job description, job requirements, salary and benefits, company information, etc.) to the user in an intuitive format. Users can view job details one by one from the recommendation list and apply. The physical significance of this step lies in transforming the results of the matching calculation into tangible and actionable value for the user, completing a closed loop from data calculation to user service. Technically, this is achieved through interaction with the user interface, displaying the job recommendation list to the user in real time. Users can also adjust the sorting dimensions according to their preferences (e.g., sorting only by ability matching degree or only by growth trend matching degree), improving the user experience. The technical effect of this step is to implement the matching calculation results of the preceding multiple steps, providing users with accurate and orderly job selection, avoiding the time cost of blindly searching through a massive number of jobs. Simultaneously, it attracts talent to companies that better match their job requirements and development potential, achieving a win-win situation for both users and companies.

[0064] like Figure 2 As shown, this application also provides a dynamic professional competence profiling and job recommendation system, including: The data acquisition module is used to acquire users' occupational data within a preset time period, including static data and dynamic behavioral data. The static capability modeling module is used to obtain work history information and professional behavior information based on the static data, obtain job named entities and keywords based on the work history information, and obtain core capability domain tags based on the job named entities and keywords. The dynamic capability evolution module is used to obtain a static capability vector based on the core capability domain labels and the professional behavior information. The professional competence profile synthesis module is used to obtain behavior sequences and behavior quantification indices based on the dynamic behavior data, obtain dynamic competence evolution vectors based on the behavior sequences and behavior quantification indices, and construct a dynamic professional competence profile based on the dynamic competence evolution vectors and static competence vectors. The job profile building module is used to obtain the user's job requirement data, obtain the job competency requirement semantic vector and job context feature value based on the job requirement data, and build a job profile based on the job competency requirement semantic vector and the job context feature value. The job recommendation module is used to match the dynamic profile of professional abilities with the job profile to obtain job matching information, and to recommend jobs to users based on the job matching information.

[0065] In one embodiment, the static capability modeling module includes: The resume semantic deconstruction unit is used to deconstruct the work resume information semantically to obtain the semantics of the company where the employee works, the semantics of the job responsibilities section, and the semantics of the projects the employee is responsible for. The company organization entity extraction unit is used to obtain the corresponding set of company organization entities based on the semantics of the company segment, wherein the company organization entities include company operation semantic terms and business unit semantic terms, and the company organization entities are used as job naming entities; The responsibility keyword extraction unit is used to perform semantic analysis on the semantics of the job responsibility segment, obtain multiple verb-noun phrase combinations of core job functions, form a responsibility keyword set from the multiple verb-noun phrases of the core job functions, and use the verb-noun phrases of the core job functions as keywords; The skill entity extraction unit is used to obtain a set of skill entities based on the semantics of the job responsibility segment. The project entity extraction unit is used to obtain a set of project entities based on the semantics of the project segment it is responsible for; The capability element set construction unit is used to merge and deduplicatize the company organization entity set, the skill entity set, the project entity set, and the responsibility keyword set to form an initial capability element set. A capability element clustering unit is used to cluster the initial set of capability elements based on a clustering algorithm to obtain multiple capability clusters and to obtain the semantic center vector of each capability cluster. The time series filtering unit is used to obtain the time information associated with the original entity in each capability cluster, obtain multiple timestamps corresponding to the multiple time information, construct a time series based on the multiple timestamps, filter the multiple semantic center vectors according to the time series based on the closest time to the current time to obtain the nearest semantic center vector, and obtain the core capability domain label based on the nearest semantic center vector.

[0066] This application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method.

[0067] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0068] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in this application and in the embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-speed SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0069] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0070] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for dynamic profiling of professional abilities and job recommendation, characterized in that, include: Acquire user occupational data within a preset time period, including static data and dynamic behavioral data; Work history information and professional behavior information are obtained from the static data, and work named entities and keywords are obtained from the work history information, and core competency domain tags are obtained from the work named entities and keywords. A static capability vector is obtained based on the core capability domain tags and the professional behavior information; Based on the dynamic behavioral data, obtain behavioral sequences and behavioral quantification indices, and based on the behavioral sequences and behavioral quantification indices, obtain dynamic capability evolution vectors, and based on the dynamic capability evolution vectors and static capability vectors, construct a dynamic profile of professional capabilities. Obtain user job requirement data, and obtain job competency requirement semantic vector and job context feature value based on the job requirement data, and construct job profile based on the job competency requirement semantic vector and the job context feature value; The job profile is matched with the job profile to obtain job matching information, and job recommendations are made to the user based on the job matching information.

2. The method for dynamic profiling of professional abilities and job recommendation according to claim 1, characterized in that, The steps of obtaining job named entities and keywords based on the job resume information, and obtaining core competency domain tags based on the job named entities and keywords, include: The work history information is semantically deconstructed to obtain semantic segments for the company where the employee works, job responsibilities, and projects undertaken. The corresponding set of company organizational entities is obtained based on the semantics of the company segment, wherein the company organizational entities include semantic terms for company operation and semantic terms for business unit, and the company organizational entities are used as job naming entities; Semantic analysis is performed on the job responsibility section to obtain multiple verb-noun phrase combinations of core job functions. These verb-noun phrases of core job functions are then combined to form a set of job responsibility keywords, and the verb-noun phrases of core job functions are used as keywords. Obtain the set of skill entities based on the semantics of the job responsibility section; Obtain the project entity set based on the semantics of the project segment you are responsible for; The set of company organizational entities, the set of skill entities, the set of project entities, and the set of responsibility keywords are merged and deduplicated to form an initial set of capability elements; The initial set of capability elements is clustered based on a clustering algorithm to obtain multiple capability clusters, and the semantic center vector of each capability cluster is obtained. Obtain the time information associated with the original entity in each capability cluster, obtain multiple timestamps corresponding to the multiple time information, construct a time series based on the multiple timestamps, filter the multiple semantic center vectors according to the time series based on the closest time to the current time to obtain the nearest semantic center vector, and obtain the core capability domain label based on the nearest semantic center vector.

3. The method for dynamic profiling of professional abilities and job recommendation according to claim 1, characterized in that, The step of obtaining a static capability vector based on the core capability domain tags and the professional behavior information includes: Based on the core competency domain tags, obtain multiple core competency assessment scores of the user within a preset time period, and calculate core competency score feature values ​​based on the multiple core competency assessment scores; Based on the professional conduct information, professional conduct characteristic items are obtained, wherein the professional conduct characteristic items include the frequency of professional conduct and the duration of professional conduct. The employment stability coefficient is obtained based on the frequency of practice and the duration of practice. The core competency score feature value and the professional stability coefficient are concatenated according to a preset time series to obtain a professional competency vector, and the professional competency vector is used as a static competency vector.

4. The method for dynamic profiling of professional abilities and job recommendation according to claim 1, characterized in that, The step of obtaining a dynamic capability evolution vector based on the behavior sequence and the behavior quantification index, and constructing a dynamic profile of professional capabilities based on the dynamic capability evolution vector and the static capability vector, includes: Multiple behavioral events and their corresponding timestamps are obtained based on the behavioral sequence, and multiple evolution intervals between the behavioral events and their adjacent behavioral events are obtained based on the multiple behavioral events and the multiple timestamps. Based on the behavior quantification index, obtain multiple behavior quantification sub-indices corresponding to each behavior event, and calculate the comprehensive quantification value of each behavior event based on the multiple behavior quantification sub-indices; Behavioral evolution feature values ​​within multiple time windows are calculated sequentially based on multiple behavioral temporal features, multiple evolution intervals, and multiple comprehensive quantization values; Time-series regression analysis was performed on multiple behavioral evolution characteristic values ​​to obtain behavioral trend indicators and behavioral fluctuation coefficients. Obtain the baseline behavioral evolution feature value of the user within the same historical time period, and calculate the behavioral evolution difference value based on the behavioral evolution feature value and the baseline behavioral evolution feature value; The behavioral trend index, the behavioral fluctuation coefficient, and the behavioral evolution difference value are vectorized and concatenated to obtain the dynamic capability evolution vector. The dynamic capability evolution vector and the static capability vector are weighted, fused, and normalized to form the dynamic profile of professional capabilities.

5. The method for dynamic profiling of professional abilities and job recommendation according to claim 1, characterized in that, The step of obtaining the job competency requirement semantic vector and job context feature value based on the job requirement data, and constructing a job profile based on the job competency requirement semantic vector and the job context feature value, includes: The job requirement data is structured and parsed to obtain a job description text segment, a job requirement text segment, and an organizational environment text segment; Semantic analysis is performed on the job description text segment and the job requirement text segment to extract multiple capability keyword entities. The capability keyword entities are then mapped to the corresponding capability dimensions according to a preset capability classification system to form an initial set of job capability requirements. Multiple organizational feature entities are extracted from the organizational environment text segment. The organizational feature entities include industry type, team size and business maturity tags, which constitute an initial set of job context feature values. Obtain the entity weights for each capability dimension in the initial set of job capability requirements, wherein the entity weights are calculated based on the frequency of the entity's occurrence in the text and a preset importance coefficient; The features of each organizational feature entity in the initial job context feature value set are quantized to obtain the corresponding job context feature value; Obtain a preset initial value for each entity weight, calculate multiple capability requirement intensity values ​​based on multiple preset initial values ​​and multiple entity weights, and merge the multiple capability requirement intensity values ​​to generate a job capability requirement semantic vector; The job context feature value and the job competency requirement semantic vector are concatenated and input into a preset job profile generation model for feature fusion and dimensionality reduction, and a structured job profile vector is output.

6. The method for dynamic profiling of professional abilities and job recommendation according to claim 1, characterized in that, The step of matching the dynamic profile of professional abilities with the job profile to obtain job matching information, and recommending jobs to users based on the job matching information, includes: Obtain the corresponding dynamic profile vector of professional ability based on the dynamic profile of professional ability; Obtain the corresponding job requirement vector based on the job profile; Based on cosine similarity, the vector similarity between the dynamic profile vector of professional ability and the vector of job requirements is calculated to obtain the ability matching score; Extract behavioral trend indicators and behavioral fluctuation coefficients from the dynamic profile of professional competence; Obtain the preset growth adaptability weights in the job profile, and calculate the growth trend matching degree based on the growth adaptability weights, the behavioral trend indicators, and the behavioral fluctuation coefficient; The ability matching score and the growth trend matching score are normalized to obtain normalized values ​​for the ability matching score and growth trend matching score. The normalized values ​​for the ability matching score and growth trend matching score are then weighted and fused to generate a comprehensive job matching score. Multiple candidate positions are obtained based on the job profile, and the multiple candidate positions are sorted according to the overall job matching degree to obtain a job recommendation list; Job recommendations are made to users based on the job recommendation list.

7. A dynamic profiling and job recommendation system for professional abilities, characterized in that, include: The data acquisition module is used to acquire users' occupational data within a preset time period, including static data and dynamic behavioral data. The static capability modeling module is used to obtain work history information and professional behavior information based on the static data, obtain job named entities and keywords based on the work history information, and obtain core capability domain tags based on the job named entities and keywords. The dynamic capability evolution module is used to obtain static capability vectors based on the core capability domain labels and the professional behavior information. The professional competence profile synthesis module is used to obtain behavior sequences and behavior quantification indices based on the dynamic behavior data, obtain dynamic competence evolution vectors based on the behavior sequences and behavior quantification indices, and construct a dynamic professional competence profile based on the dynamic competence evolution vectors and static competence vectors. The job profile building module is used to obtain the user's job requirement data, obtain the job competency requirement semantic vector and job context feature value based on the job requirement data, and build a job profile based on the job competency requirement semantic vector and the job context feature value. The job recommendation module is used to match the dynamic profile of professional abilities with the job profile to obtain job matching information, and to recommend jobs to users based on the job matching information.

8. The vocational ability dynamic profiling and job recommendation system according to claim 7, characterized in that, The static capability modeling module includes: The resume semantic deconstruction unit is used to deconstruct the work resume information semantically to obtain the semantics of the company where the employee works, the semantics of the job responsibilities section, and the semantics of the projects the employee is responsible for. The company organization entity extraction unit is used to obtain the corresponding set of company organization entities based on the semantics of the company segment, wherein the company organization entities include company operation semantic terms and business unit semantic terms, and the company organization entities are used as job naming entities; The responsibility keyword extraction unit is used to perform semantic analysis on the semantics of the job responsibility segment, obtain multiple verb-noun phrase combinations of core job functions, form a responsibility keyword set from the multiple verb-noun phrases of the core job functions, and use the verb-noun phrases of the core job functions as keywords; The skill entity extraction unit is used to obtain a set of skill entities based on the semantics of the job responsibility segment. The project entity extraction unit is used to obtain a set of project entities based on the semantics of the project segment it is responsible for; The capability element set construction unit is used to merge and deduplicatize the company organization entity set, the skill entity set, the project entity set, and the responsibility keyword set to form an initial capability element set. A capability element clustering unit is used to cluster the initial set of capability elements based on a clustering algorithm to obtain multiple capability clusters and to obtain the semantic center vector of each capability cluster. The time series filtering unit is used to obtain the time information associated with the original entity in each capability cluster, obtain multiple timestamps corresponding to the multiple time information, construct a time series based on the multiple timestamps, filter the multiple semantic center vectors according to the time series based on the closest time to the current time to obtain the nearest semantic center vector, and obtain the core capability domain label based on the nearest semantic center vector.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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