Intelligent diagnosis and optimization method for resume

Abstract

The invention discloses an intelligent diagnosis and optimization method for a resume. The method comprises the following steps: obtaining a resume file and a target post description corresponding to the resume file; performing information extraction on the resume file to obtain structured resume information; performing quantitative evaluation on the structured resume information to generate a diagnosis result; performing semantic matching analysis on the structured resume information and the target post description, and identifying an information gap; based on the diagnosis result and the information gap, performing reconstruction processing on experience description in the structured resume information; and based on the diagnosis result, the information gap and the reconstructed experience description, generating and outputting an optimization suggestion for the resume file. According to the method, accurate diagnosis and optimization of the resume are realized, and the adaptation degree of the resume and the post is improved.

Description

Technical Field

[0001] This invention relates to the field of data processing, and in particular to an intelligent method for diagnosing and optimizing resumes. Background Technology

[0002] In the job search process, resumes are the core basis for recruiters to initially screen candidates, and their quality and suitability for the position directly determine the success rate. Currently, most students lack professional resume writing and optimization knowledge, and their core difficulties lie in two aspects: first, they cannot accurately identify the weaknesses in their own resumes; second, they do not know how to adjust the content in a targeted manner according to the requirements of the target position. This results in resumes that generally lack a prominent core advantage and whose content is out of touch with the job requirements, making it difficult to stand out in the competition.

[0003] Existing resume assistance tools for students have significant limitations. They only provide formatted templates—helping students standardize resume formatting and layout—but cannot conduct in-depth analysis based on the target job description. These tools cannot diagnose or assess resume quality, identify gaps in the fit between resumes and job requirements, or provide targeted content optimization guidance. They completely fail to address students' core pain points: not knowing how to revise or the revised version not being suitable.

[0004] Therefore, students urgently need a method that can combine the requirements of target positions to achieve intelligent resume diagnosis and precise content optimization, in order to replace existing tools that can only provide formatted templates, help students clarify the direction of modification, improve the suitability of resumes to positions, and thus increase the job application success rate. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent diagnosis and optimization method for resumes, addressing the aforementioned problems. This method can combine the requirements of the target job to achieve intelligent diagnosis and precise content optimization of resumes, replacing existing tools that can only provide formatted templates. It helps students clarify the direction of modification and improve the suitability of their resumes for the job.

[0006] The technical solution adopted in this invention is as follows:

[0007] A method for intelligent diagnosis and optimization of resumes, characterized by the following steps:

[0008] S1: Obtain the resume file and the corresponding target job description;

[0009] S2: Extract information from the resume file to obtain structured resume information;

[0010] S3: Quantitatively evaluate the structured resume information and generate diagnostic results;

[0011] S4: Perform semantic matching analysis between the structured resume information and the target job description to identify information gaps;

[0012] S5: Based on the diagnostic results and information gaps, the experience descriptions in the structured resume information are reconstructed;

[0013] S6: Based on the diagnostic results, information gaps, and reconstructed experience descriptions, generate and output optimization suggestions for the resume file.

[0014] Furthermore, step S1 specifically includes the following steps:

[0015] S1.1: Receive resume files uploaded by users and verify the format compatibility and completeness of the resume files;

[0016] S1.2: Receive job filtering information set by the user, wherein the job filtering information includes at least the job name and industry category;

[0017] S1.3: Based on the job screening information, obtain or receive the target job description corresponding to the resume file;

[0018] S1.4: Associate the verified resume file with the target job description as input for subsequent processing.

[0019] Furthermore, step S2 specifically includes the following steps:

[0020] S2.1: Extract the original text data according to the format category of the resume file, using the text extraction method corresponding to the format category;

[0021] S2.2: Preprocess the original text data to obtain preprocessed text data; the preprocessing includes word segmentation and stop word removal;

[0022] S2.3: Extract entity information of multiple preset categories from the preprocessed text data;

[0023] S2.4: The extracted entity information is deduplicated and standardized to obtain integrated entity information;

[0024] S2.5: Organize the integrated entity information into structured data with unified fields to obtain the structured resume information.

[0025] Furthermore, step S3 specifically includes the following steps:

[0026] S3.1: Parse the structured resume information, and determine the professional background or skill background associated with the structured resume information based on the educational background information or skill description information extracted from the structured resume information;

[0027] S3.2: Based on the professional background or skill background and the target job description, invoke multiple preset evaluation dimensions;

[0028] S3.3: For each evaluation dimension, calculate the quantitative score of the structured resume information on that evaluation dimension;

[0029] S3.4: Based on the quantitative scores of each assessment dimension, generate diagnostic results that identify the shortcomings of each assessment dimension.

[0030] Furthermore, step S4 specifically includes the following steps:

[0031] S4.1: Convert the structured resume information into a first semantic vector, and convert the target job description into a second semantic vector;

[0032] S4.2: Calculate the overall matching degree between the first semantic vector and the second semantic vector;

[0033] S4.3: Determine the matching level based on the comparison between the overall matching degree and the preset threshold;

[0034] S4.4: If it is determined to be a partial match, then through fine-grained comparison, the missing items, weak items and redundant items in the structured resume information relative to the target job description are identified as the information gaps.

[0035] Furthermore, step S5 specifically includes the following steps:

[0036] S5.1: Extract the shortcomings of each evaluation dimension of the structured resume information from the diagnostic results, and extract the information gap items that need to be supplemented or strengthened from the information gaps;

[0037] S5.2: Based on the shortcomings of each evaluation dimension and the information gap items, locate the experience description text to be optimized from the structured resume information;

[0038] S5.3: Analyze the experience description text to identify the behavioral elements to be strengthened and the outcome elements to be supplemented;

[0039] S5.4: Based on the preset optimization framework, the identified behavioral elements to be strengthened are enhanced, the identified outcome elements to be supplemented are supplemented, and the enhanced behavioral elements and the supplemented outcome elements are reorganized according to the optimization framework to generate a reconstructed experience description.

[0040] Furthermore, step S6 specifically includes the following steps:

[0041] S6.1: Based on the diagnostic results, information gaps, and reconstructed experience description, generate an operation set containing specific modification items;

[0042] S6.2: Determine the optimal execution mode;

[0043] S6.3: Execute or guide the execution of the set of operations according to the determined optimization execution mode, and output the optimized resume file or optimization guidelines as optimization suggestions for the resume file.

[0044] Furthermore, the method also includes:

[0045] S7: Update the knowledge data supporting the quantitative evaluation, semantic matching analysis and reconstruction processing, and update the steps for performing the quantitative evaluation, the semantic matching analysis and the reconstruction processing.

[0046] Furthermore, step S7 specifically includes the following steps:

[0047] S7.1: Regularly collect updated job description and skill terminology data from external data sources to update the knowledge data;

[0048] S7.2: Collect user feedback on the adoption of the optimization suggestions and the effect of submitting the resume file to form feedback data;

[0049] S7.3: Based on the feedback data, adjust and update the weights and parameters in the quantitative evaluation, the semantic matching analysis, and the reconstruction process.

[0050] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0051] This invention upgrades resume processing from traditional formatted templates to an intelligent diagnostic and precise optimization system that combines target job descriptions by constructing a closed-loop process of "data acquisition - structured processing - quantitative evaluation - semantic matching - experience reconstruction - optimized output - dynamic update". Its core feature is to connect the technical links of "evaluation - matching - optimization - iteration" to achieve a leap from "format standardization" to "precise content adaptation".

[0052] Because this method processes the entire process around the target job description, it identifies the shortcomings of the resume file itself through quantitative assessment, locates the fit gap with the job through semantic matching, and then carries out targeted reconstruction based on the dual analysis results. Finally, it continuously improves the effect through dynamic updates. Therefore, it can effectively solve the core pain points of students "unable to identify the shortcomings of the resume file" and "not knowing how to adjust the content in a targeted manner", significantly improve the content quality and job fit of the resume file, thereby increasing the job application success rate, and replacing the existing tools that can only provide formatted templates. Attached Figure Description

[0053] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0055] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0056] like Figure 1 As shown, this invention discloses an intelligent diagnosis and optimization method for resumes, comprising the following steps:

[0057] S1: Obtain the resume file and the corresponding target job description;

[0058] S2: Extract information from the resume file to obtain structured resume information;

[0059] S3: Quantitatively evaluate the structured resume information and generate diagnostic results;

[0060] S4: Perform semantic matching analysis between the structured resume information and the target job description to identify information gaps;

[0061] S5: Based on the diagnostic results and information gaps, the experience descriptions in the structured resume information are reconstructed;

[0062] S6: Based on the diagnostic results, information gaps, and reconstructed experience description, generate and output optimization suggestions for the resume file;

[0063] S7: Update the knowledge data supporting the quantitative evaluation, semantic matching analysis and reconstruction processing, and update the steps for performing the quantitative evaluation, the semantic matching analysis and the reconstruction processing.

[0064] This invention upgrades resume processing from traditional formatted templates to an intelligent diagnostic and precise optimization system that combines target job descriptions by constructing a closed-loop process of "data acquisition - structured processing - quantitative evaluation - semantic matching - experience reconstruction - optimized output - dynamic update". Its core feature is to connect the technical links of "evaluation - matching - optimization - iteration" to achieve a leap from "format standardization" to "precise content adaptation".

[0065] Because this method processes the entire process around the target job description, it identifies the shortcomings of the resume file itself through quantitative assessment, locates the fit gap with the job through semantic matching, and then carries out targeted reconstruction based on the dual analysis results. Finally, it continuously improves the effect through dynamic updates. Therefore, it can effectively solve the core pain points of students "unable to identify the shortcomings of the resume file" and "not knowing how to adjust the content in a targeted manner", significantly improve the content quality and job fit of the resume file, thereby increasing the job application success rate, and replacing the existing tools that can only provide formatted templates.

[0066] Furthermore, step S1 specifically includes the following steps:

[0067] S1.1: Receive resume files uploaded by users and verify the format compatibility and completeness of the resume files;

[0068] S1.2: Receive job filtering information set by the user, wherein the job filtering information includes at least the job name and industry category;

[0069] S1.3: Based on the job screening information, obtain or receive the target job description corresponding to the resume file;

[0070] S1.4: Associate the verified resume file with the target job description as input for subsequent processing.

[0071] This invention ensures that the input resume file is valid data for subsequent processing by verifying format compatibility and integrity; it accurately locates the user's target job direction by receiving the job screening information set by the user, and obtains the corresponding target job description based on the job screening information; it associates the verified resume file with the target job description, providing a unified input data foundation for all subsequent analysis steps.

[0072] By excluding invalid resume files with incompatible formats or incomplete content, analysis failures due to data issues in subsequent processing are avoided. By linking job screening information with target job descriptions, it is ensured that subsequent quantitative assessments, semantic matching analyses, and other steps are all centered around the user's actual target job, thus improving the method's relevance and reliability.

[0073] Furthermore, step S2 specifically includes the following steps:

[0074] S2.1: Extract the original text data according to the format category of the resume file, using the text extraction method corresponding to the format category;

[0075] S2.2: Preprocess the original text data to obtain preprocessed text data; the preprocessing includes word segmentation and stop word removal;

[0076] S2.3: Extract entity information of multiple preset categories from the preprocessed text data;

[0077] S2.4: The extracted entity information is deduplicated and standardized to obtain integrated entity information;

[0078] S2.5: Organize the integrated entity information into structured data with unified fields to obtain the structured resume information.

[0079] This invention purifies the original text data through preprocessing such as word segmentation and stop word removal to obtain preprocessed text data. Then, entity information is extracted, deduplicated, and standardized to obtain integrated entity information. The integrated entity information is then organized into structured data with unified fields to obtain structured resume information, which makes the resume information have the characteristics of being standardized, quantifiable, and comparable.

[0080] Because structured resumes solve the problems of inconsistent formats and fragmented information in traditional resumes, they provide a standard data foundation for subsequent quantitative evaluation (which requires scoring based on unified fields) and semantic matching analysis (which requires comparison based on standardized information). Therefore, they can significantly improve the accuracy of subsequent quantitative evaluation and the efficiency of semantic matching analysis, ensuring the smooth implementation of subsequent technical steps, while providing users with a clear and standardized information presentation format.

[0081] Furthermore, step S2.1 specifically includes the following steps:

[0082] S2.1.1: If the resume file is in image format, then call the optical character recognition model to perform text recognition and obtain the original text data;

[0083] S2.1.2: If the resume file is in Word or PDF format, the corresponding document parsing library will be called to directly extract the text content;

[0084] S2.1.3: If the resume file is in TXT format, the file content is directly read as the raw text data.

[0085] This invention designs differentiated text extraction schemes for three mainstream resume file formats that users may upload: images, Word / PDF, and TXT. For image formats, text recognition is achieved through OCR technology, while for TXT formats, text is extracted through a document parsing library or by direct reading, ensuring the effectiveness of text extraction from resume files of different formats.

[0086] Because this method achieves full compatibility with mainstream resume formats, it can accurately extract the original text data regardless of whether the user uploads a scanned document (image format), a commonly edited document (Word / PDF), or a plain text file (TXT). Therefore, it expands the applicability of the entire method, avoids the problem of users being unable to use it due to format restrictions, and improves the user experience and the practicality of the method.

[0087] Furthermore, step S2.4 specifically includes the following steps:

[0088] S2.4.1: Calculate the SimHash fingerprint value of each entity information;

[0089] S2.4.2: Calculate the Hamming distance between any two fingerprint values;

[0090] S2.4.3: If the Hamming distance is less than or equal to a preset threshold (range 3-5), it is determined to be duplicate information and the content is merged.

[0091] This invention generates a unique fingerprint value for each entity information using the SimHash algorithm, uses Hamming distance to measure information similarity, sets a reasonable threshold (3-5) to accurately identify duplicate entity information, and merges duplicate content to remove redundancy from the data level.

[0092] Because SimHash fingerprints and Hamming distance can efficiently and accurately identify duplicate information in resumes (such as repeatedly listed skills or the same project experience mentioned multiple times), the merged entity information is more concise and accurate. This avoids the interference of duplicate information on subsequent quantitative evaluation (such as the score of the redundancy dimension) and semantic matching analysis (such as the matching degree distortion caused by keyword repetition), ensuring the quality of structured resume information and improving the accuracy of subsequent analysis steps.

[0093] Furthermore, step S3 specifically includes the following steps:

[0094] S3.1: Parse the structured resume information, and determine the professional background or skill background associated with the structured resume information based on the educational background information or skill description information extracted from the structured resume information;

[0095] S3.2: Based on the professional background or skill background and the target job description, invoke multiple preset evaluation dimensions;

[0096] S3.3: For each evaluation dimension, calculate the quantitative score of the structured resume information on that evaluation dimension;

[0097] S3.4: Based on the quantitative scores of each assessment dimension, generate diagnostic results that identify the shortcomings of each assessment dimension.

[0098] This invention first determines the user's professional or skill background through the structured resume information, and then calls up multiple targeted evaluation dimensions (such as content completeness, job suitability, etc.) in conjunction with the target job description. The performance of the structured resume information in each evaluation dimension is quantified by quantitative scoring. Finally, the scores are integrated to generate a diagnostic result that includes overall quality and shortcomings in each evaluation dimension. The core is to achieve a precise correlation between "background-job-evaluation".

[0099] Since the evaluation dimensions are dynamically invoked based on the user's professional or skill background and the target job description, the quantitative score can objectively reflect the performance of the structured resume information in the core evaluation dimensions. The diagnostic results clearly point out the overall quality and the shortcomings of each evaluation dimension. Therefore, it solves the core pain point that students cannot identify the shortcomings of their own resume files, provides clear direction for subsequent optimization steps, ensures that the optimization work is targeted, and improves optimization efficiency.

[0100] Furthermore, step S3.3 specifically includes the following steps:

[0101] S3.3.1: Dimensional rule matching: For each evaluation dimension, the structured resume information is compared with the preset quantitative scoring rules to obtain the basic rule quantitative score. The quantitative scoring rules are a set of standard rules pre-set based on industry standards for evaluating the quality of specific aspects of the resume (such as "deduct 20 points for missing one core field in the content completeness evaluation dimension, and deduct 10 points for insufficient skill description").

[0102] S3.3.2: Semantic relevance calculation: For the evaluation dimensions involving content description (job suitability, skill highlight prominence), calculate the relevance of the relevant descriptions in the resume file and the target job description in the semantic space, and obtain a quantitative score of relevance in the semantic space;

[0103] S3.3.3: Unique Value Assessment: Identify information items in the structured resume that are significantly different from the content of a standard resume template with the same professional background or skill background as unique items, and calculate their unique value bonus.

[0104] S3.3.4: Weighted Fusion of Scores: The quantitative scores of the basic rules, the quantitative scores of the relevance in the semantic space, and the unique value bonus are weighted and fused according to the preset weights for each evaluation dimension to obtain the final quantitative score of the evaluation dimension.

[0105] This invention adopts a three-dimensional scoring fusion mechanism of "basic rules + semantic analysis + unique value". The basic rules match to ensure that the basic quality of the resume file meets the standards, the semantic relevance calculation reflects the content fit with the target job description, and the unique value assessment highlights the differentiated advantages. After weighted fusion, a comprehensive and accurate final quantitative score is obtained.

[0106] By breaking through the limitations of traditional single-rule evaluation, this method ensures that resumes meet basic industry standards through fundamental rules, aligns with the target job description requirements through semantic analysis, and encourages students to highlight their individual strengths through unique value assessment. Therefore, the final quantitative score can comprehensively and objectively reflect the overall quality of resumes, making the diagnostic results more valuable and providing a more accurate basis for subsequent optimization, thus enhancing the core competitiveness of the entire method.

[0107] Furthermore, step S4 specifically includes the following steps:

[0108] S4.1: Convert the structured resume information into a first semantic vector, and convert the target job description into a second semantic vector;

[0109] S4.2: Calculate the overall matching degree between the first semantic vector and the second semantic vector (calculated using the cosine similarity algorithm);

[0110] S4.3: Based on the comparison between the overall matching degree and the preset threshold, determine the matching level (high matching, partial matching, no matching);

[0111] S4.4: If it is determined to be a partial match, then through fine-grained comparison, the missing items, weak items and redundant items in the structured resume information relative to the target job description are identified as the information gaps.

[0112] This invention first converts the structured resume information and the target job description into a first semantic vector and a second semantic vector, respectively. It then calculates the overall matching degree between the first semantic vector and the second semantic vector using cosine similarity and determines the matching level. For the most common partial matching scenarios, it accurately identifies missing items (job description exists, but the structured resume information does not), weak items (insufficient matching degree), and redundant items (structured resume information exists, job description does not exist and is duplicated) through fine-grained comparison, thus clarifying the specific type of information gap.

[0113] Since semantic vector transformation and cosine similarity calculation can objectively reflect the overall semantic fit between the two, fine-grained comparison can further locate the specific information gap type. Therefore, it solves the pain point of students "not knowing how to adjust the content according to the job requirements", and provides a targeted basis for subsequent reconstruction processing and optimization suggestions. This ensures that the optimization work can accurately fill the information gap and improve the fit between the structured resume information and the target job description.

[0114] Furthermore, the fine-grained comparison in step S4.4 specifically includes the following steps:

[0115] S4.4.1: Perform deep semantic parsing on the target job description to extract multiple requirement dimensions, including at least core skill requirements, competency requirements, and years of experience requirements;

[0116] S4.4.2: For each requirement dimension, retrieve the description content of the corresponding dimension from the structured resume information;

[0117] S4.4.3: A multidimensional evaluation model is used to calculate the matching degree between the retrieved description content and the demand dimension. The multidimensional evaluation model includes at least three evaluation sub-models: keyword matching degree, semantic similarity degree, and experience fit degree.

[0118] S4.4.4: Based on the preset threshold rules, classify according to the matching degree calculation results:

[0119] If the matching degree is lower than the first threshold (range 0-0.3), it is determined to be a missing item;

[0120] If the matching degree is between the first threshold and the second threshold (range 0.3-0.7), it is determined to be a weak point;

[0121] If the matching degree is higher than the third threshold (range 0.9-1.0) and there are too many repeated descriptions in the structured resume information, it is judged as a redundant item.

[0122] This invention uses deep semantic parsing to break down the target job description into multiple requirement dimensions. For each dimension, corresponding content is retrieved from the structured resume information. A multi-dimensional evaluation model of "keyword matching + semantic similarity + experience fit" is used to calculate the matching degree. Then, a three-level threshold rule is used to accurately classify missing items, weak items, and redundant items, thereby achieving fine-grained and accurate identification of the information gap.

[0123] By breaking down the target job description into specific dimensions, the multidimensional evaluation model avoids the limitations of a single matching standard. The three-level threshold rule clarifies the criteria for determining the type of information gap, thus enabling precise identification of specific problems in each requirement dimension of the structured resume information (such as missing core skills, weak experience fit, and redundant descriptions). This makes the identification of information gaps more granular and operable, providing a more specific and clear direction for subsequent reconstruction processing and improving the accuracy of optimization.

[0124] Furthermore, in the partially matched state, a priority score P is calculated for each identified information gap item, with the following formula:

[0125]

[0126] in:

[0127] P: Priority score for information gap items, ranging from 0 to 1. A higher score indicates that the item needs to be processed with higher priority.

[0128] t: The skill or requirement terminology corresponding to the information gap item;

[0129] IDF(t): Inverse document frequency of term t in the job corpus; the calculation formula is IDF(t)=log(M / (Nt+1)), where M is the total number of documents in the job corpus and Nt is the number of documents containing t;

[0130] I pos (t): The syntactic position weight of term t in the target job description, where it is 1.0 when it appears in the title, 0.8 when it appears in the job requirements, 0.6 when it appears in the core responsibilities, and 0.3 when it appears in other positions;

[0131] d(t,D r ): Term t and resume text D r The average semantic distance is calculated using Euclidean distance and ranges from 0 to 10.

[0132] D max : Normalization constant, preset value is 10;

[0133] ω1, ω2: Balance weight coefficients, satisfying ω1+ω2=1; default values ​​are ω1=0.6, ω2=0.4.

[0134] This invention quantifies the priority of information gap items through a formula, integrating three major factors: term scarcity (IDF value), job requirement importance (syntactic position weight), and resume file relevance (semantic distance), and obtains priority scores through weight balancing and normalization.

[0135] Since priority scores objectively reflect the impact of each information gap on job suitability, information gaps with higher scores (such as the lack of scarce skills in job requirements) require priority processing. This helps students clarify the optimization order among numerous information gaps, avoid blind modifications, and ensure that optimization efforts are focused on core information gaps, thereby improving optimization efficiency and effectiveness. Furthermore, step S5 specifically includes the following steps:

[0136] S5.1: Extract the shortcomings of each evaluation dimension of the structured resume information from the diagnostic results, and extract the information gap items that need to be supplemented or strengthened from the information gaps;

[0137] S5.2: Based on the shortcomings of each evaluation dimension and the information gap items, locate the experience description text to be optimized from the structured resume information;

[0138] S5.3: Analyze the experience description text to identify the behavioral elements to be strengthened and the outcome elements to be supplemented;

[0139] S5.4: Based on the preset optimization framework, the identified behavioral elements to be strengthened are enhanced, the identified outcome elements to be supplemented are supplemented, and the enhanced behavioral elements and supplemented outcome elements are reorganized according to the optimization framework to generate a reconstructed experience description; and the quality of the reconstructed experience description of a single experience is quantitatively evaluated using a highlight scoring formula. The core variables of the highlight scoring formula include the TF-IDF value of the core behavioral verb, the quantitative score of the outcome, the normalized length of the description, and the semantic similarity between the outcome and the job requirements.

[0140] This invention uses the shortcomings in each assessment dimension of the diagnostic results and the information gaps in the information gaps as a guide to locate the experience description text that needs optimization, analyze and identify the behavioral elements that need to be strengthened and the achievement elements that need to be supplemented. Based on a preset optimization framework, the behavioral elements that need to be strengthened are enhanced to improve their professionalism, and the achievement elements that need to be supplemented are added to make up for the gaps in job requirements. Finally, the enhanced behavioral elements and the added achievement elements are reorganized according to the optimization framework to generate a reconstructed experience description.

[0141] Because the optimization process closely revolves around the shortcomings and information gaps in each evaluation dimension, the restructured experience descriptions are more aligned with job requirements and better reflect individual strengths, effectively solving the problems of insufficient highlighting of core strengths and disconnection from job requirements in experience descriptions.

[0142] Furthermore, the highlight score H used in step S5.4 to evaluate the quality of a single experience reconstruction is calculated using the following formula:

[0143]

[0144] in:

[0145] H: The score for the highlights described after reconstruction, ranging from 0 to 1. The higher the score, the more prominent the highlights.

[0146] TF-IDF verb The TF-IDF value of the core behavioral verbs in the experience description in the industry verb library reflects the professionalism of the behavior, with a value range of 0-1;

[0147] Q score The quantitative score for the results is determined by the following rules: 1.0 for quantification with specific numerical values, 0.6 for quantification with fuzzy expressions, and 0.2 for no quantification.

[0148] L norm The normalized length of the described text is calculated using the formula Lnorm = L / L. avg Where L is the number of characters in the current experience description, L avg The average number of characters used to describe similar experiences;

[0149] Sim result The semantic similarity between the "results" section of the reconstructed experience description and the core requirements of the target job description, with a value range of 0-1.

[0150] This invention constructs a performance evaluation system for experience descriptions using formulas, focusing on behavioral professionalism (TF-IDF verbs, such as "leading" and "optimizing" which score higher) and quantifiable results (Q). score The highest scores were for specific numerical quantification and concise description (L). norm Avoid lengthy or short texts), and ensure job relevance (Sim). result The restructuring effect is evaluated from four core dimensions: the degree to which the results match the job requirements.

[0151] Since the highlight score can quantify the quality of the reconstructed experience description, the higher the score, the more outstanding the experience description and the more it fits the job requirements. Therefore, it provides a clear optimization standard for experience description reconstruction, guiding students to pay attention to the use of professional verbs, quantitative expression of results, conciseness and job relevance during the reconstruction process, so as to ensure that the reconstructed experience description has the characteristics of "highlighting highlights and strong adaptability", thus improving the competitiveness of resume files.

[0152] Furthermore, the preset optimization framework is the STAR framework, namely Situation, Task, Action, and Result. The reconstructed experience description must fully include the above four elements, and the "Result" element must specifically fill the missing needs corresponding to the information gaps.

[0153] This invention uses the STAR framework as an optimization framework for experience description, requiring that the reconstructed experience description fully include the four elements of context, task, action, and result, and that the "result" element specifically fills in information gaps (such as missing skills or weak experience).

[0154] Since the STAR framework is a recognized high-quality experience description framework in the recruitment field, the complete presentation of the four elements can make the experience description logically clear and well-organized, and the "result" element can specifically fill in the information gaps, so that the reconstructed experience description has both a standardized structure and can accurately meet the job requirements, significantly improving the professionalism and persuasiveness of the experience description.

[0155] Furthermore, step S6 specifically includes the following steps:

[0156] S6.1: Based on the diagnostic results, information gaps, and reconstructed experience description, generate an operation set containing specific modification items;

[0157] S6.2: Determine the optimal execution mode;

[0158] S6.3: Execute or guide the execution of the set of operations according to the determined optimization execution mode, and output the optimized resume file or optimization guidelines as optimization suggestions for the resume file.

[0159] Based on the diagnostic results, information gaps, and reconstructed experience description, this invention generates an operation set containing specific modifications. After determining the optimized execution mode, the operation set is executed or guided according to the determined mode, and finally an optimized resume file or optimization guidelines are output as optimization suggestions for the resume file.

[0160] Because the operation set clearly defines the specific modifications, the optimized execution mode meets the operational needs of different users, and the output results are directly available to users (the optimized resume file can be submitted directly, and the optimization guide can instruct users to modify it themselves), it solves the pain point of students "not knowing how to modify" and ensures that the optimization suggestions can be effectively implemented.

[0161] Furthermore, step S6.2 specifically includes the following steps:

[0162] S6.2.1: Provides a one-click optimization mode selection. The system automatically completes all modifications and directly generates an optimized resume file (supports Word and PDF format downloads).

[0163] S6.2.2: Provides a step-by-step guided mode selection. The system provides interactive guidance (including modification location, problem description, and optimization template) to guide users to complete the optimization operation step by step and preview the modification effect in real time.

[0164] This invention provides two optimization execution modes: the one-click optimization mode automatically executes a set of operations to quickly generate an optimized resume file; the step-by-step guided mode guides users to manually complete the modifications through interactive instructions (modification location, problem description, optimization template) and real-time preview.

[0165] The one-click optimization mode meets the needs of users who want to quickly obtain optimized resume files, while the step-by-step guided mode meets the needs of users who want to independently participate in modifying and learning resume file optimization methods. This expands the applicable user group and improves the flexibility and satisfaction of the user experience.

[0166] Furthermore, step S7 specifically includes the following steps:

[0167] S7.1: Regularly collect updated job description and skill terminology data from external data sources (mainstream recruitment platforms, industry occupational skill standard libraries) to update the knowledge data;

[0168] S7.2: Collect user feedback on the adoption of the optimization suggestions and the effect of submitting the resume (whether an interview invitation was received, the interview pass rate), and form feedback data;

[0169] S7.3: Based on the feedback data, adjust and update the weights and parameters in the quantitative evaluation, the semantic matching analysis, and the reconstruction process.

[0170] In this invention, knowledge data is collected from mainstream recruitment platforms and industry standard libraries to ensure the authority and timeliness of the data; feedback data includes user adoption feedback and job search effect feedback to ensure that the update direction is in line with actual use scenarios and results; weights and parameters are adjusted based on feedback data to achieve precise optimization of the core process.

[0171] Because the updated knowledge data ensures that the quantitative assessment dimensions, semantic matching keywords, and reconstructed terminology all conform to current industry standards and job requirements, and the parameter adjustments in the core process are based on real job application feedback data, the diagnostic accuracy, matching accuracy, and optimization effectiveness of the method can be continuously improved. This allows the method to dynamically adapt to changes in the recruitment market and user needs, ensuring its long-term usability.

[0172] Furthermore, in step S7.3, the adjustment and update of the weights and parameters are achieved by minimizing the following loss function L:

[0173]

[0174] in:

[0175] L: Loss function value; the smaller the value, the better the model's prediction accuracy and generalization ability.

[0176] M: Number of samples in the feedback data (total number of historical optimization cases within an optimization cycle).

[0177] y m ∈{0,1}: The true quality label of the m-th sample, determined based on the feedback data in step S7.2 (1 indicates positive feedback, 0 indicates no positive feedback);

[0178] σ(⋅): Sigmoid activation function, calculated as σ(x) = 1 / (1+e^(x-x)). -x This is used to map predicted scores to the 0-1 interval;

[0179] S m The quantitative evaluation process is to predict the overall evaluation score for the m-th sample.

[0180] W: The weight parameter vector to be optimized, including the weights of each evaluation dimension, the fitness weighting coefficient, etc.

[0181] The squared L2 norm of vector W is used to limit the size of the parameter weights;

[0182] η: L2 regularization coefficient, with a value range of 0.001-0.01.

[0183] This invention optimizes weight parameters by minimizing the loss function of "cross-entropy loss + L2 regularization". Cross-entropy loss accurately measures the deviation between the model's predicted score (overall evaluation score) and the actual job application results (whether there is positive feedback or not), ensuring that the parameter adjustment direction is in line with the actual effect. L2 regularization limits the scale of parameter weights, prevents the model from overfitting historical feedback data, and ensures the generalization ability to new samples.

[0184] Since the adjustment of the weight parameters is based on both real job application feedback and generalization ability, it can achieve stable iterative optimization of the parameters, avoid performance fluctuations caused by parameter drift, ensure continuous improvement in the performance of core processes such as quantitative evaluation, semantic matching, and reconstruction processing, and at the same time ensure the adaptability of the method to different professional or skill backgrounds, different positions, and different resume files, thereby improving the robustness and reliability of the method.

[0185] Furthermore, the knowledge data includes a professional skills dictionary for a specific educational stage or professional field, and its construction and updating process specifically includes the following steps:

[0186] Terminology was collected from authoritative sources (higher education professional talent training programs, industry vocational skill standards, and job requirements from mainstream recruitment platforms);

[0187] Semantic clustering (using the K-means algorithm) and standardization (synonym merging and error correction) are performed on the terms.

[0188] Establish a mapping relationship between terminology and professions / job positions to form a structured skills dictionary.

[0189] This invention clarifies the construction and updating process of a professional skills dictionary. It collects terms from authoritative sources such as higher education programs, industry standards, and recruitment platforms, and ensures the standardization and accuracy of terms through K-means clustering and standardization. It establishes a mapping relationship between terms and majors and positions, so that the dictionary has a three-dimensional association feature of "major-position-term".

[0190] Since the professional skills dictionary is the core knowledge data support for quantitative assessment (such as the scoring of the skill highlight salience dimension), semantic matching analysis (such as the extraction and comparison of core skill keywords), and reconstruction processing (such as the standardized use of professional terms), its authority, standardization, and relevance can directly improve the accuracy and professionalism of the above core steps. Therefore, it ensures that the technical foundation of the entire method is solid and reliable, and provides high-quality knowledge data guarantee for the final diagnostic results and optimization suggestions.

[0191] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for intelligent diagnosis and optimization of resumes, characterized in that, Includes the following steps: S1: Obtain the resume file and the corresponding target job description; S2: Extract information from the resume file to obtain structured resume information; S3: Quantitatively evaluate the structured resume information and generate diagnostic results; S4: Perform semantic matching analysis between the structured resume information and the target job description to identify information gaps; S5: Based on the diagnostic results and information gaps, the experience descriptions in the structured resume information are reconstructed; S6: Based on the diagnostic results, information gaps, and reconstructed experience descriptions, generate and output optimization suggestions for the resume file.

2. The method according to claim 1, characterized in that, Step S1 specifically includes the following steps: S1.1: Receive resume files uploaded by users and verify the format compatibility and completeness of the resume files; S1.2: Receive job filtering information set by the user, wherein the job filtering information includes at least the job name and industry category; S1.3: Based on the job screening information, obtain or receive the target job description corresponding to the resume file; S1.4: Associate the verified resume file with the target job description as input for subsequent processing.

3. The method according to claim 1, characterized in that, Step S2 specifically includes the following steps: S2.1: Extract the original text data according to the format category of the resume file, using the text extraction method corresponding to the format category; S2.2: Preprocess the original text data to obtain preprocessed text data; the preprocessing includes word segmentation and stop word removal; S2.3: Extract entity information of multiple preset categories from the preprocessed text data; S2.4: The extracted entity information is deduplicated and standardized to obtain integrated entity information; S2.5: Organize the integrated entity information into structured data with unified fields to obtain the structured resume information.

4. The method according to claim 1, characterized in that, Step S3 specifically includes the following steps: S3.1: Parse the structured resume information, and determine the professional background or skill background associated with the structured resume information based on the educational background information or skill description information extracted from the structured resume information; S3.2: Based on the professional background or skill background and the target job description, invoke multiple preset evaluation dimensions; S3.3: For each evaluation dimension, calculate the quantitative score of the structured resume information on that evaluation dimension; S3.4: Based on the quantitative scores of each assessment dimension, generate diagnostic results that identify the shortcomings of each assessment dimension.

5. The method according to claim 1, characterized in that, Step S4 specifically includes the following steps: S4.1: Convert the structured resume information into a first semantic vector, and convert the target job description into a second semantic vector; S4.2: Calculate the overall matching degree between the first semantic vector and the second semantic vector; S4.3: Determine the matching level based on the comparison between the overall matching degree and the preset threshold; S4.4: If it is determined to be a partial match, then through fine-grained comparison, the missing items, weak items and redundant items in the structured resume information relative to the target job description are identified as the information gaps.

6. The method according to claim 1, characterized in that, Step S5 specifically includes the following steps: S5.1: Extract the shortcomings of each evaluation dimension of the structured resume information from the diagnostic results, and extract the information gap items that need to be supplemented or strengthened from the information gaps; S5.2: Based on the shortcomings of each evaluation dimension and the information gap items, locate the experience description text to be optimized from the structured resume information; S5.3: Analyze the experience description text to identify the behavioral elements to be strengthened and the outcome elements to be supplemented; S5.4: Based on the preset optimization framework, the identified behavioral elements to be strengthened are enhanced, the identified outcome elements to be supplemented are supplemented, and the enhanced behavioral elements and the supplemented outcome elements are reorganized according to the optimization framework to generate a reconstructed experience description.

7. The method according to claim 1, characterized in that, Step S6 specifically includes the following steps: S6.1: Based on the diagnostic results, information gaps, and reconstructed experience description, generate an operation set containing specific modification items; S6.2: Determine the optimal execution mode; S6.3: Execute or guide the execution of the set of operations according to the determined optimization execution mode, and output the optimized resume file or optimization guidelines as optimization suggestions for the resume file.

8. The method according to claim 1, characterized in that, The method further includes: S7: Update the knowledge data supporting the quantitative evaluation, semantic matching analysis and reconstruction processing, and update the steps for performing the quantitative evaluation, the semantic matching analysis and the reconstruction processing.

9. The method according to claim 8, characterized in that, Step S7 specifically includes the following steps: S7.1: Regularly collect updated job description and skill terminology data from external data sources to update the knowledge data; S7.2: Collect user feedback on the adoption of the optimization suggestions and the effect of submitting the resume file to form feedback data; S7.3: Based on the feedback data, adjust and update the weights and parameters in the quantitative evaluation, the semantic matching analysis, and the reconstruction process.

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