A big data analysis and mining method and system for talent recruitment

Abstract

This invention relates to the technical field of big data analysis and mining, and provides a method and system for big data analysis and mining for talent recruitment. The method includes: acquiring text information from job applicants; organizing the text information to form corresponding text data; performing contextual analysis on critical expressions in the text data; determining the value of critical expressions based on the contextual analysis results, obtaining value determination results; associating critical expressions with valuable value determination results with the applicant's ability characteristics; marking critical expressions with non-valuable value determination results as potential risk warnings; integrating ability characteristics with assessment information from other sources for the applicant; adjusting the weight of different assessment information during the integration process according to the specific requirements of the target position, and processing traditional career path information to obtain integrated assessment information of the applicant's corresponding ability characteristics. This invention improves the accuracy of talent identification.

Description

Technical Field

[0001] This invention relates to the technical field of big data analysis and mining, specifically to a big data analysis and mining method and system for talent recruitment. Background Technology

[0002] In the talent recruitment process, existing data processing systems face challenges in evaluating candidates, especially in identifying innovative talent with non-traditional professional backgrounds or who demonstrate critical thinking in professional discussions. These systems often struggle to accurately integrate information from diverse sources and may misinterpret certain expressions, leading to the underestimation or loss of high-potential talent.

[0003] Specifically, existing big data analytics systems for talent recruitment struggle to resolve evaluation conflicts between information from different sources when simultaneously assessing applicants based on both their traditional professional resumes and their professional activities on publicly available online platforms. For instance, when the system generates a competency assessment based on an applicant's publicly available technical contributions that demonstrates high levels of professional skills and innovative potential, while a second assessment based on their resume shows poor performance in terms of career stability, the existing system struggles to effectively integrate and weigh these contradictory assessments.

[0004] Furthermore, existing big data analytics systems for talent recruitment fail to accurately distinguish between technical criticism and negative personal emotions when analyzing candidates' publicly posted text content. These systems are prone to misinterpreting profound criticism within a professional field as negative behavioral characteristics, such as simply labeling critical statements as "negative emotions" or "aggressive expressions," and inputting them as potential "teamwork risks" into the candidate's overall profile. This unfair evaluation ultimately leads to the failure to identify high-potential, multi-talented individuals with atypical career paths, causing companies to miss out on the talent they truly need.

[0005] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0006] This application discloses a big data analysis and mining method and system for talent recruitment, which aims to solve the challenges faced by enterprises in evaluating candidates during the talent recruitment process, especially in identifying innovative talents with non-traditional professional backgrounds or who demonstrate critical thinking in professional discussions. Existing systems have difficulty accurately integrating information from different sources and may misinterpret certain expressions, leading to the underestimation or loss of high-potential talents.

[0007] The technical solution of this application is as follows: Firstly, this application discloses a big data analysis and mining method for talent recruitment, including: Obtain the text information corresponding to job applicants on online platforms; Organize the text information to form corresponding text data; Perform contextual analysis on critical expressions in text data; contextual analysis includes: identifying whether the object targeted by the critical expression is a technical concept, obtaining object identification results; determining whether the intent of the critical expression is to solve a problem or propose an improvement, obtaining intent judgment results; identifying whether the critical expression contains solutions or improvement suggestions, obtaining solution suggestion identification results; analyzing the logical structure of the critical expression, obtaining logical analysis results; and organizing the object identification results, intent judgment results, solution suggestion identification results, and logical analysis results into corresponding contextual analysis results. Based on the results of contextual analysis, the value of critical expression is determined, resulting in a value determination. The value assessment results are translated into valuable critical expressions and linked to the applicant's competencies. Critical expressions that are deemed to lack value are marked as potential risk warnings. Integrate competency characteristics with assessment information from other sources regarding the applicant; Based on the specific requirements of the target position, the weight of different assessment information in the integration process is adjusted, and the traditional career path information is processed to obtain the corresponding ability characteristic assessment integration information of the applicant.

[0008] This technical solution can effectively distinguish between technical criticism and negative personal emotions, avoid misinterpreting profound criticism within a professional field as negative behavioral characteristics, thereby more accurately identifying high-potential talent and resolving evaluation conflicts between assessment information from different sources.

[0009] Secondly, this application also discloses a big data analysis and mining system for talent recruitment, used to perform big data analysis and mining for talent recruitment, including: The text information acquisition module is used to acquire the text information corresponding to the applicants on the online platform; The text data formation module is used to organize text information and form corresponding text data; The context analysis execution module is used to perform context analysis on critical expressions in text data. Context analysis includes: identifying whether the object targeted by the critical expression is a technical concept, obtaining object identification results; determining whether the intent of the critical expression is to solve a problem or propose an improvement, obtaining intent judgment results; identifying whether the critical expression contains solutions or improvement suggestions, obtaining solution suggestion identification results; analyzing the logical structure of the critical expression, obtaining logical analysis results; and organizing the object identification results, intent judgment results, solution suggestion identification results, and logical analysis results into the corresponding context analysis results. The value determination execution module is used to determine the value of critical expressions based on the results of context analysis, and obtain the value determination result; The competency-based association module is used to associate the value assessment results, which are valuable critical expressions, with the competency characteristics of the applicant. The risk warning labeling module is used to mark critical expressions that are deemed to have no value as potential risk warnings. The assessment information fusion module is used to integrate competency characteristics with assessment information from other sources for the applicant; The integrated information processing module is used to adjust the weight of different assessment information during the integration process according to the specific requirements of the target position, and to process traditional career path information to obtain the corresponding integrated assessment information of the applicant's ability characteristics.

[0010] This technical solution provides a system-level solution that enables the effective implementation of big data analysis and mining methods in talent recruitment, thereby improving recruitment efficiency and talent matching.

[0011] Beneficial Effects: The big data analysis and mining method for talent recruitment disclosed in this application acquires text information from applicants on online platforms and conducts in-depth contextual analysis of their critical expressions. This allows for accurate identification of the target audience, intent, whether solutions or improvement suggestions are included, and the logical structure of these critical expressions. This meticulous contextual analysis avoids the problem of existing systems misinterpreting profound criticism within a professional field as negative emotion. It thus associates truly valuable critical expressions with the applicant's competency characteristics, while marking unvaluable expressions as potential risk warnings. Furthermore, this application integrates applicant competency characteristics with assessment information from other sources and adjusts the weight of different assessment information according to the specific requirements of the target position. It also processes traditional career path information, effectively resolving the problem of evaluation conflicts between different sources in existing technologies. Through the above technical solutions, this application can more comprehensively and accurately assess the overall capabilities and potential of applicants, especially in identifying innovative talents with non-traditional professional backgrounds or who demonstrate critical thinking in professional discussions. This overcomes the shortcomings of existing technologies, preventing high-potential talent from being underestimated or missed, thereby enabling companies to recruit the talent they truly need. Attached Figure Description

[0012] Figure 1 This is a flowchart of a big data analysis and mining method for talent recruitment, as described in one embodiment of the present invention. Figure 2 This is a flowchart of a big data analysis and mining method for talent recruitment according to another embodiment of the present invention; Figure 3 This is a system block diagram of a big data analysis and mining system for talent recruitment according to another embodiment of the present invention; Explanation of reference numerals in the attached figures: 1. Big data analysis and mining system for talent recruitment; 11. Text information acquisition module; 12. Text data formation module; 13. Context analysis execution module; 14. Value determination execution module; 15. Ability characteristic association module; 16. Risk warning marking module; 17. Assessment information fusion module; 18. Fusion information processing module. Detailed Implementation

[0013] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0014] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0015] Traditional data processing systems for talent recruitment face numerous challenges in evaluating candidates, particularly in identifying innovative individuals with non-traditional professional backgrounds or who demonstrate critical thinking in professional discussions. These systems struggle to accurately integrate information from diverse sources and may misinterpret certain expressions, leading to the underestimation or loss of high-potential talent. Specifically, existing systems are significantly inadequate in handling evaluation conflicts between publicly disclosed technical contributions and traditional professional resume information, and in distinguishing between technical criticism and negative personal sentiments, thus failing to identify high-potential talent.

[0016] In response, this application proposes a big data analysis and mining method for talent recruitment, combining... Figure 1 As shown, it includes: S1, obtain the text information of the applicant on the online platform; S2, organize the text information to form corresponding text data; S3, Perform contextual analysis on critical expressions in the text data; contextual analysis includes: identifying whether the object targeted by the critical expression is a technical concept, obtaining object identification results; determining whether the intent of the critical expression is to solve a problem or propose an improvement, obtaining intent judgment results; identifying whether the critical expression contains solutions or improvement suggestions, obtaining solution suggestion identification results; analyzing the logical structure of the critical expression, obtaining logical analysis results; and organizing the object identification results, intent judgment results, solution suggestion identification results, and logical analysis results into corresponding contextual analysis results. S4. Based on the results of contextual analysis, determine the value of critical expression and obtain the value determination results; S5, value identification results are expressed as valuable critical expressions and linked to the candidate's competency characteristics; S6 marks critical expressions that determine value as having no value as potential risk warnings; S7 integrates competency characteristics with assessment information from other sources for the applicant; S8. Based on the specific requirements of the target position, adjust the weight of different assessment information in the integration process, and process the traditional career path information to obtain the corresponding ability characteristic assessment integration information of the applicant.

[0017] "Textual information" refers to the written content that applicants publicly publish, comment on, or participate in discussions on various online platforms. This textual information typically originates from public channels such as technical forums, blogs, social media, open-source communities, or technical Q&A platforms, and may include technical problem analysis, experience summaries, solution comparisons, defect discussions, or improvement suggestions. This type of textual information can reflect the applicant's focus on technical issues, depth of thought, and expression style over a relatively long period of time.

[0018] "Critical expression" refers to the questions, criticisms, reflections, improvement suggestions, or comparative analyses raised by the applicant regarding a particular technology, product, system architecture, implementation plan, or technical viewpoint in the aforementioned text information. This type of expression is not simply a negative evaluation, but rather focuses on problem analysis, risk disclosure, or optimization ideas, demonstrating the applicant's independent judgment, technical insight, and potential problem-solving abilities when faced with existing solutions.

[0019] "Contextual analysis" refers to the process of understanding and reconstructing the context in which a critical expression is situated after it has been identified, using multi-dimensional analytical methods to accurately determine its true intent, motivation, logical structure, and potential technological value. This analytical process is not based on emotional polarity or simple keyword judgments, but rather combines semantics, target audience, intent, and logical relationships to provide a structured interpretation of the critical expression, thereby avoiding the misjudgment of constructive technical criticism as negative attitudes or risky behaviors.

[0020] In the specific implementation process, the first step is to obtain the applicant's corresponding text information on online platforms. This acquisition process can be achieved in various ways, such as periodically or in real-time collecting publicly posted text content from recruitment-related or technology-related online platforms using pre-defined network interface rules. As a preferred method, authorized public data can be obtained through the platform's application programming interface (API), or applicants can proactively provide their personal homepage identification information on online platforms, allowing the system to collect their historical text content in a targeted manner, thereby ensuring the legality and integrity of the data source.

[0021] After acquiring the text information, it is organized and preprocessed to form corresponding text data. This organization process includes, but is not limited to, cleaning the original text to remove advertising content, irrelevant links, emoticons, and noise information that is obviously unrelated to the technical discussion; at the same time, it performs word segmentation, part-of-speech tagging, and named entity recognition on the text to transform unstructured text into structured text data with clear semantic units and grammatical tags, providing a unified data foundation for subsequent analysis.

[0022] Subsequently, a critical expression contextual analysis was performed on the processed text data. This contextual analysis process includes at least the following aspects of analysis: First, identify whether the object of the critical expression is a technical concept, and obtain the object identification result accordingly. By identifying and classifying the entities involved in the text, determine whether it refers to a specific technical object, such as a programming language, algorithm model, system architecture, development framework, toolchain, or technical solution, thereby distinguishing between technical criticism and non-technical emotional expression.

[0023] Secondly, it is necessary to determine whether the intent of critical expression is to solve problems or propose improvements, and to obtain the intent judgment result accordingly. This judgment is made by analyzing whether the expression contains semantic features such as problem identification, cause analysis, improvement hypothesis, or optimization direction, thereby distinguishing between constructive technical discussion and simple emotional venting.

[0024] Third, identify whether the critical statements contain clear solutions or improvement suggestions, and based on this, obtain the solution recommendation identification results. This identification focuses on whether the statements specifically describe alternative solutions, implementation paths, parameter adjustment methods, or technology selection suggestions, in order to determine whether the applicant has the ability to transform problems into executable solutions.

[0025] Fourth, analyze the logical structure of critical expressions to obtain logical analysis results. Through syntactic structure analysis, paragraph structure analysis, or argumentation relationship analysis, determine whether the expression has a clear argument, reasonable supporting evidence, and a coherent reasoning process, thereby assessing its technical argumentation ability and rigor of expression.

[0026] The object identification results, intent judgment results, solution suggestion identification results, and logical analysis results are all organized into corresponding contextual analysis results and stored in a structured form for unified retrieval and comprehensive evaluation in subsequent steps.

[0027] After obtaining the contextual analysis results, the value of the critical expression is determined based on these results, resulting in a value assessment. This value assessment process can be implemented through pre-set evaluation rules or models, comprehensively considering whether the critical expression addresses a technical issue, whether it has a clear problem-solving or improvement intention, whether it proposes specific solutions, and the completeness and rigor of its logical structure, thereby quantitatively evaluating the technical value of the critical expression. Generally, critical expressions that address technical problems, have a constructive improvement intention, and are logically clear are considered to have high value.

[0028] For critical expressions that demonstrate value in their value assessment, these should be linked to the applicant's competency profile. Specifically, based on the technical field, problem type, and solution approach involved in the critical expression, the applicant's competency profile in the relevant technical areas should be enhanced or marked accordingly, so that their competency profile reflects their performance in practical technical thinking and problem analysis.

[0029] Critical expressions that do not demonstrate value in the value assessment are marked as potential risk warnings. This marking does not directly negate the candidate's abilities, but rather indicates potential areas for improvement in communication style, expression habits, or emotional management, serving as supplementary information in the recruitment evaluation.

[0030] Subsequently, the aforementioned competency characteristics are integrated with evaluation information from other sources. This other evaluation information may include resume information, interview feedback, written test scores, project experience, or technical test results. The integration process is achieved through weighted, normalized, or multi-source feature fusion models, enabling comprehensive analysis of evaluation results from different sources and dimensions within the same evaluation system.

[0031] Finally, based on the specific competency requirements of the target position, the weights of different assessment information during the integration process are dynamically adjusted, and traditional career path information is debiased to obtain integrated assessment information on the applicant's corresponding competency characteristics. In this way, the system avoids simply judging candidates based on job stability or superficial resumes, but instead combines their technical development trajectory and critical thinking skills to form a more objective, comprehensive assessment result that is highly aligned with the job requirements.

[0032] In some implementations, to improve the stability and consistency between contextual analysis results and value determination results, the system can also introduce a cross-time consistency verification mechanism. This mechanism is used to analyze whether multiple critical expressions from the same applicant at different times and on different platforms exhibit a consistent technical focus, depth of problem analysis, or evolution path of improvement ideas. Through this consistency verification, occasional expressions and long-term technical thinking patterns can be effectively distinguished, thereby further improving the reliability of the results related to competency characteristics. At the same time, this mechanism can also correct for possible accidental biases in individual expressions during the value determination stage, avoiding over-amplification or misjudgment of competency characteristics due to single textual information.

[0033] Optional, combined Figure 2 As shown, the steps for performing contextual analysis of critical expressions in textual data include: A1, obtain the applicant's interaction records on online platforms; A2, organize the interaction records to form corresponding interaction data; A3, Identify the relationships between interaction records in the interaction data; A4, construct the applicant's thought process based on the relationships between the interaction records; A5, Analyze the response behaviors triggered by the thinking path in the community; A6. Based on the response behavior, assess the constructiveness of the thinking path to obtain a constructive assessment result; A7. Constructive assessment results are used to establish constructive thinking paths and are linked to the applicant's competencies. A8 marks constructive assessment results that are not constructive thinking paths as potential risk warnings.

[0034] Specifically, acquiring applicants' interaction records on online platforms refers to the system collecting dynamic data from multiple pre-set online platforms, reflecting applicants' participation in various technical interactive activities. These online platforms may include, but are not limited to, technical forums, open-source communities, code hosting platforms, professional social media, and technical Q&A platforms. The interaction records can cover various forms of interaction, such as text comments, questions and answers, code commit records, code review comments, defect reports, solution discussion processes, and project merging records. These interaction records can reflect, from a behavioral perspective, the depth of the applicant's participation, thinking methods, and collaborative habits in real-world technical scenarios.

[0035] After obtaining the aforementioned interaction records, these records are organized to form corresponding interaction data. This organization process includes data cleaning, invalid content removal, duplicate record merging, format standardization, and timestamp calibration of the original interaction records. This transforms interaction records from different sources and with varying structures into a unified data structure. For example, each interaction record can be organized into an information unit containing the participating entities, the time of occurrence, the interaction type, a content summary, and a linking identifier. Through this step, the originally discrete and unstructured interaction information is transformed into structured interaction data that can be used for system analysis, providing a data foundation for subsequent relationship identification and path construction.

[0036] Furthermore, the relationships between various interaction records within the interactive data are identified. This relationship identification is achieved through semantic analysis, citation relationship analysis, and behavioral association analysis of the interaction data, used to determine whether reply relationships, citation relationships, collaboration relationships, or correspondences between questions and solutions exist between different interaction records. For example, by analyzing reply chains, the association between code submissions and question numbers, comment citation tags, or merged records, it can be determined whether a particular interaction record responds to or supplements another interaction record. By identifying the above relationships, an association structure between interaction records is formed.

[0037] After identifying the relationships between the interaction records, the candidate's thought process is constructed based on these relationships. This thought process involves linking a set of logically related interaction records according to chronological order and causal relationships, forming a sequential structure that reflects the candidate's evolving technical thinking. This thought process demonstrates the candidate's complete journey from identifying a problem, analyzing it, proposing hypotheses, providing solutions, responding to feedback, to ultimately reaching a stable conclusion or having it adopted by the community, thus revealing their problem-solving abilities, technical reasoning skills, and continuous improvement capabilities. Furthermore, the thought process also reflects how the candidate interacts with others in a collaborative environment, absorbs feedback, and adjusts their solutions.

[0038] After constructing the thought process path, the response behavior triggered by this path in the community is further analyzed. This response behavior refers to the feedback actions taken by other community members or platform systems regarding the thought process path, including but not limited to liking, commenting, forwarding, quoting, adopting, merging code, changing the issue status, or referencing it in subsequent discussions. By analyzing the type, frequency, duration, and participating entities of the response behavior, the scope of influence and degree of acceptance of the thought process path in the community can be assessed, thereby reflecting its actual technical value.

[0039] Based on the analysis of response behavior, the constructiveness of the thinking paths is evaluated, resulting in a constructive assessment. This constructive assessment is not solely based on the number of responses, but rather considers whether the response behavior demonstrates effective problem-solving, adopted solutions, or the formation of technical consensus. For example, thinking paths that drive problem-solving, facilitate code merging, or spark high-quality technical discussions are typically assessed as highly constructive; while thinking paths that merely generate controversy, fail to produce effective solutions, or remain at the problem description level for an extended period may be assessed as less constructive.

[0040] After obtaining constructive assessment results, constructive thinking paths are linked to the candidate's competency characteristics. Specifically, based on the behavioral characteristics and technical content reflected in the thinking path, the candidate's competency characteristics in areas such as problem-solving, technological innovation, collaboration, and technological influence are enhanced or marked accordingly, ensuring that the candidate's competency profile reflects their comprehensive performance in real-world technical interaction environments. Conversely, constructive assessment results indicating unconstructive thinking paths are marked as potential risk warnings, suggesting that the candidate may have factors requiring further attention in areas such as technical judgment, solution feasibility, or communication methods, thus providing prudent reference in subsequent talent evaluation processes.

[0041] In some preferred embodiments, suppose a job applicant participates in a series of technical discussions surrounding "database query performance optimization" on a technical forum for an open-source project. The system first obtains the applicant's relevant posts, replies, and corresponding code review and submission records from the forum, forming raw interaction records. These records are then organized and transformed into structured interaction data containing author, time, interaction type, content summary, and association identifiers. Based on this, the system identifies relationships between the interaction records; for example, the applicant first analyzes performance bottlenecks, then provides optimization solutions, supplements their arguments and code examples after being challenged, and finally, the example is adopted and merged by the project maintainers. The system constructs a complete thought process path for the applicant, from problem analysis, solution proposal, feedback response to implementation, and further analyzes the response behavior triggered by this thought process in the community, such as the number of likes, positive comments, and subsequent citations. Based on the above response behavior, the system assesses that the thought process path is highly constructive and associates it with the applicant's "database optimization ability," "complex problem-solving ability," and "technical influence." Conversely, if a thought process remains merely at the stage of complaining about problems, and the proposed solutions lack feasibility and are not accepted by the community, then that thought process is assessed as unconstructive and marked as a potential risk warning for comprehensive consideration in subsequent talent assessments.

[0042] Optional steps in analyzing the response behaviors triggered by thought processes within a community include: Obtain information on the professional scope of the technical issues involved in the thought process; Obtain information on community activity related to the technical issues involved in the thought process; Based on information about the scope of professional fields and community activity levels, determine whether to activate the low activity compensation mechanism; When the low-activity compensation mechanism is activated, the evaluation weight of the number of response behaviors is reduced; Identify and quantify response behaviors that demonstrate professional recognition, in-depth thinking, or substantial impact; Track whether the thought process has been indirectly adopted by other related technical discussions or projects to obtain reference information; After reducing the evaluation weight of the number of response behaviors and identifying and quantifying response behaviors that reflect professional recognition, deep thinking, or substantial impact, the response behaviors triggered by thinking paths in the community are analyzed and evaluated based on tracking reference information.

[0043] Specifically, obtaining information on the professional field scope of the technical issues involved in the thought process refers to identifying which professional fields (e.g., artificial intelligence, blockchain, cloud computing) the technical topics discussed in the thought process belong to. Simultaneously, obtaining information on the community activity of the technical issues involved in the thought process involves collecting data such as the frequency of discussion, number of participants, and content update speed of the technical issues in relevant communities to reflect their current level of activity.

[0044] The system determines whether to activate a low-activity compensation mechanism based on information about the scope of the professional field and community activity. The aim is to avoid underestimating valuable thought processes due to insufficient response volume in highly specialized fields with low community activity. For example, when a technical issue is highly specialized and community activity is below a preset threshold, the system will trigger the low-activity compensation mechanism.

[0045] When the low activity compensation mechanism is activated, the evaluation weight of the number of response behaviors is reduced. This means that when evaluating the constructiveness of the thinking path, the number of response behaviors is no longer over-relyed on. Instead, more weight is allocated to the quality and depth of the response behaviors.

[0046] Furthermore, identifying and quantifying response behaviors that demonstrate professional recognition, in-depth thinking, or substantial impact refers to conducting in-depth analysis of response behaviors such as comments, likes, citations, and code submissions in the community to identify those that truly reflect professional recognition, contain in-depth technical analysis, or have a substantial impact on the project. For example, a detailed technical commentary given by a domain expert is far more valuable than a simple like from multiple ordinary users.

[0047] Furthermore, tracking whether thought processes are indirectly adopted by other related technical discussions or projects provides valuable reference information. The aim is to capture the long-term and implicit impact of these thought processes. For example, an innovative idea proposed early in the development process might only be adopted by a project team months later and reflected in code refactoring or new feature implementation. This indirect adoption is a significant manifestation of its value.

[0048] Ultimately, after reducing the evaluation weight of the quantity of response behaviors and identifying and quantifying response behaviors that demonstrate professional recognition, deep thinking, or substantial impact, a comprehensive analysis and evaluation of the response behaviors triggered by the thought process within the community is conducted based on tracking reference information. This yields a more comprehensive, accurate, and fair evaluation result, thereby more effectively identifying the applicant's true abilities and potential value.

[0049] Optionally, the steps to track whether the thought process has been indirectly adopted by other related technical discussions or projects, and to obtain tracking reference information, include: Continuously monitor the project's code repository commit history and version control history; Based on the monitored commit records and version control history, identify code refactoring, new feature implementation, or design pattern changes related to innovative ideas in the applicant's thought process; Analyze the correspondence between code refactoring, new feature implementation, or design pattern changes and the innovative ideas proposed in the candidate's thought process; Based on the correspondence, the substantive impact of implicit adoption is assessed according to the scale, complexity, and degree of impact on key modules of the project, such as code refactoring, new feature implementation, or design pattern changes, and tracking reference information is obtained.

[0050] Specifically, continuously monitoring the commit history and version control history of a project's code repository refers to using automated tools or APIs to obtain all code commit records, branch merge history, tag release information, and file modification logs from the target project's code repository in real time or periodically. The purpose is to establish a comprehensive code change database, providing foundational data for subsequent analysis.

[0051] Based on monitored commit records and version control history, identifying code refactoring, new feature implementations, or design pattern changes related to the innovative ideas in the applicant's thought process can be understood as using Natural Language Processing (NLP) technology and code analysis tools to perform semantic analysis on commit information, code comments, and documentation changes, combined with code structure analysis, to identify code refactoring (such as module decoupling, performance optimization), new feature implementations (such as adding modules, extending existing functions), or design pattern changes (such as switching from the singleton pattern to the factory pattern) that are related to the specific innovative ideas proposed by the applicant in their thought process (e.g., a new algorithm, an optimized architecture, a solution to a specific technical problem) in terms of function, structure, or implementation. The goal is to accurately locate specific implementations that may be influenced by the applicant's ideas from a massive amount of code changes.

[0052] In practical applications, analyzing the correspondence between code refactoring, new feature implementation, or design pattern changes and the innovative ideas proposed in the candidate's thought process involves comparing the identified code changes with the innovative ideas described in the candidate's thought process. This can be achieved through methods such as keyword matching, semantic similarity calculation, and structured comparison to quantify the degree of correlation. For example, if a candidate proposes an "event-driven microservice architecture optimization scheme," and the code repository contains numerous code refactorings and new feature implementations related to message queues, service registration and discovery, and asynchronous communication, then a strong correspondence can be considered between the two. The aim is to establish a clear link between innovative ideas and actual code implementation.

[0053] Furthermore, based on the corresponding relationships, the substantive impact of implicit adoption is assessed according to the scale, complexity, and impact on key project modules of code refactoring, new feature implementation, or design pattern changes, yielding tracking reference information. Specifically, scale can refer to the number of lines of code, files, and modules involved; complexity can refer to the logical complexity of the code change and the breadth of the technology stack involved; and the impact on key project modules can refer to whether the change affects core business logic, underlying framework, or frequently used common components. By comprehensively evaluating these factors, the actual value and far-reaching impact of the implicit adoption on the project can be quantified, thus obtaining highly reliable tracking reference information. The aim is to ensure that the evaluation of indirect adoption goes beyond the surface and delves into its contribution to the core value of the project.

[0054] Optionally, based on information about the scope of professional fields and community activity, the steps to determine whether to activate the low activity compensation mechanism include: When receiving the interaction records of applicants on the online platform, we can obtain information in real time about the professional field of the technical issues involved in the interaction records and the community activity information. Based on information on the scope of professional fields and community activity, and in conjunction with preset judgment rules, determine whether to activate the low activity compensation mechanism; When it is determined that a low activity compensation mechanism needs to be activated, record the current timestamp and start a timer for the compensation mechanism to take effect; During the timer period for the compensation mechanism to take effect, continuous monitoring of changes in information related to the scope of professional fields and community activity will be conducted. When changes in information within a professional field or community activity level exceed a threshold, causing the conditions for activating the low activity compensation mechanism to be unmet, the low activity compensation mechanism is terminated, and a termination timestamp is recorded.

[0055] Specifically, when the system receives interaction records from applicants on online platforms, it is configured to acquire real-time information on the professional field scope and community activity of the technical topics involved in the interactions. Real-time acquisition aims to ensure that the information obtained is up-to-date and reflects the current technological environment and community dynamics. Professional field scope information can be understood as the sub-technical field, related technology stack, or application scenario to which the technical topic belongs, such as "edge computing security." Community activity information refers to indicators such as the discussion popularity, number of participants, frequency of new posts, and number of replies in relevant technical communities.

[0056] The system uses information on the scope of the professional field and community activity, combined with pre-defined judgment rules, to determine whether to activate a low-activity compensation mechanism. These pre-defined rules can be a series of logical conditions. For example, if a technical topic is identified as highly niche and community activity is below a certain threshold (e.g., fewer than 30 discussion posts per month, or fewer than 50 core participants), then the low-activity compensation mechanism is deemed necessary. This judgment rule aims to identify scenarios where apparent low activity levels are due to a narrow or emerging field, but which may actually contain high-value insights.

[0057] In practical applications, when it is determined that a low-activity compensation mechanism needs to be activated, the system is configured to record the current timestamp and start a compensation mechanism activation timer. The timestamp is used to record the precise point in time when the compensation mechanism begins to take effect, for subsequent time series analysis or auditing. The compensation mechanism activation timer can be a software timer used to track the duration of the compensation mechanism, for example, set to 24 hours, 7 days, or longer, during which time the low-activity compensation mechanism will continue to operate.

[0058] Furthermore, during the timer period for the compensation mechanism to take effect, the system is configured to continuously monitor changes in professional domain-specific information and community activity information. Continuous monitoring means that the system periodically (e.g., hourly, daily) reacquires and analyzes this information to capture its dynamic changes. This monitoring mechanism ensures the adaptability of the compensation strategy, enabling it to adjust according to the evolution of the community environment.

[0059] Furthermore, when changes exceeding thresholds are detected in information regarding the scope of a professional field or community activity, causing the conditions for activating the low-activity compensation mechanism to be no longer met, the system is configured to terminate the low-activity compensation mechanism and record the termination timestamp. Changes exceeding the threshold can refer to a sudden and significant increase in community activity, such as a doubling of discussion volume within a short period, or the professional field being identified as having expanded to a broader audience and no longer belonging to a niche category. When these changes render the original conditions for activating the compensation mechanism no longer met, the compensation mechanism will be automatically shut down to avoid overcompensation or inaccurate assessment, and the termination timestamp will be recorded for retrospective purposes.

[0060] Optional steps to identify and quantify response behaviors that demonstrate professional recognition, deep thinking, or substantial impact include: Obtain the applicant's interaction history on online platforms; Perform content type identification on interaction records to distinguish between text-based and non-text-based interactive content; When non-text interactive content is identified, the non-text interactive content is transformed into various structured data. The data transformation process includes: extracting comments from code submissions to identify technical discussions, problem feedback, or design intentions; parsing annotations in design drafts to identify design optimization suggestions, functional improvement points, or technical questions; and performing semantic analysis on the transcripts of oral discussions to identify professional viewpoints, argumentation processes, or solutions. Content analysis is performed on the various structured data after data transformation to identify whether they contain detailed demonstrations of technical solutions, in-depth analysis of existing problems, or predictions of future development trends, thereby obtaining content-based response behavior identification results. The system identifies whether authoritative literature, industry standards, or cutting-edge research results are cited in the structured data after data transformation, and obtains the citation-based response behavior identification results. The innovation, feasibility, and potential impact on the technology community of the improvement suggestions or solutions proposed from the various structured data after quantification data transformation.

[0061] Specifically, obtaining applicants' interaction records on online platforms means that the system collects all interactive information publicly posted or discussed by applicants from various online platforms through interfaces. These interaction records may include, but are not limited to, code submissions, comments, question answers, article publications, design annotations, meeting recordings, etc.

[0062] Among them, the content type identification of interaction records, distinguishing between text-based and non-text-based interaction content, can be understood as the system first preprocessing the acquired raw interaction records, and automatically determining whether they are plain text (such as forum posts, code comments) or non-text (such as design drafts, audio files, code repositories) by means of file extension, MIME type, content characteristics, etc.

[0063] In practical applications, when non-textual interactive content is identified, it undergoes data transformation into structured data. The aim is to unify non-textual information of different formats into a structured format suitable for subsequent analysis. Specifically, extracting comments from code submissions to identify technical discussions, feedback, or design intent involves using Natural Language Processing (NLP) technology to parse the developer's thought process, encountered problems, and solutions from the code comments. Parsing annotations in design drafts to identify design optimization suggestions, functional improvements, or technical questions involves using image recognition and text extraction technology to extract improvement opinions or questions about the design scheme from the annotation areas of the design drafts. Semantic analysis of recorded oral discussions to identify professional viewpoints, argumentation processes, or solutions involves converting the audio file into text and then using semantic analysis technology to identify the professional insights, argumentation logic, and proposed specific solutions contained within.

[0064] Furthermore, content analysis is performed on the structured data after data transformation to identify whether it contains detailed arguments for technical solutions, in-depth analysis of existing problems, or predictions of future development trends. This yields content-based response behavior identification results, the purpose of which is to assess the depth and breadth of the applicant's technical discussions. This can be achieved through techniques such as keyword matching, topic modeling, sentiment analysis, and deep learning models to identify high-quality, insightful content.

[0065] Furthermore, the system identifies whether the structured data after data transformation cites authoritative literature, industry standards, or cutting-edge research findings, thus obtaining citation-based response behavior identification results. The purpose of this is to measure the breadth of an applicant's knowledge and professional rigor. The system can establish an authoritative knowledge base and determine whether these resources are cited in the interactive content through text matching or semantic similarity calculation.

[0066] Finally, the innovativeness, feasibility, and potential impact on the technical community of the improvement suggestions or solutions proposed from the structured data after quantification are assessed. This aims to comprehensively evaluate the value of the applicant's contribution. Innovativeness can be evaluated through its differences from existing solutions and its originality; feasibility can be assessed through dimensions such as technology maturity, resource requirements, and implementation difficulty; and potential impact can be assessed based on its guiding role in community discussion, the universality of the problem solved, and its alignment with future technological development trends.

[0067] Optional steps to quantify the innovativeness, feasibility, and potential impact on the technical community of proposed improvement suggestions or solutions from the structured data after data transformation include: Obtain suggestions or solutions for improvement from job applicants; Perform semantic analysis on improvement suggestions or solutions to extract the corresponding core innovations; The core innovations are compared with the mainstream technological paradigms in the current technology field to obtain the comparison results; Based on the comparison results, identify whether there are conflicts in the function, structure or principle of the core innovation points; When the identified conflicts reach a preset threshold, the long-term value assessment mode is activated. Under the long-term value assessment model, forward-looking analysis is conducted on improvement suggestions or solutions to obtain forward-looking analysis results. Forward-looking analysis includes: assessing the maturity of the underlying technologies or theories on which the core innovation points depend; predicting the potential application scenarios of the core innovation points in future technology development trends; identifying the possibility of industry changes or the establishment of new standards triggered by the core innovation points; and analyzing the impact of the core innovation points on the existing ecosystem. Based on the results of the prospective analysis, the long-term potential value of the improvement suggestions or solutions is quantified.

[0068] Specifically, obtaining improvement suggestions or solutions from job applicants refers to identifying and extracting all suggestions or solutions explicitly raised by applicants in their interaction records from structured data after data transformation. These suggestions or solutions aim to optimize existing technologies, solve specific problems, or introduce new features. These suggestions or solutions can be textual descriptions, code snippets, design draft annotations, or key arguments from oral discussion transcripts. Semantic analysis of the improvement suggestions or solutions to extract the corresponding core innovation points can be understood as using Natural Language Processing (NLP) technology to conduct deep semantic analysis of the suggestions or solutions proposed by job applicants, identifying their inherent innovative elements, key technological breakthroughs, or unique thinking patterns. For example, techniques such as keyword extraction, topic modeling, and entity recognition can be used to accurately locate the core innovations that distinguish them from existing technologies from complex descriptions. In practical applications, comparing the core innovation points with mainstream technological paradigms in the current technology field and obtaining comparison results refers to systematically comparing the extracted core innovation points with currently widely accepted and applied technical standards, architectural patterns, mainstream algorithms, or design concepts within the industry. The comparison results can be quantified as similarity scores, difference indicators, or the degree of potential conflict. Further, based on the comparison results, it is determined whether the core innovation points conflict in function, structure, or principle. The purpose is to determine whether the innovation point is fundamentally incompatible with or disruptive to the existing technological paradigm. For example, a solution that completely replaces existing modules functionally, a solution that requires a complete restructuring of the system architecture structurally, or a new theory that challenges traditional understanding in principle may all be identified as conflicting. When the identified conflicts reach a preset threshold, a long-term value assessment mode is initiated. Its purpose is to conduct a more in-depth examination of innovation points that may be disruptive but are not widely accepted initially. The preset threshold can be set based on industry experience, technology maturity curves, or expert evaluation. Under the long-term value assessment mode, forward-looking analysis is conducted on improvement suggestions or solutions to obtain forward-looking analysis results. The purpose is to assess their potential value from a more macro and long-term perspective. Forward-looking analysis includes: assessing the maturity of the underlying technologies or theories upon which the core innovation relies, such as examining whether it is based on emerging but not yet fully validated technologies, or whether it depends on scientific theories that are still under development; predicting the potential application scenarios of the core innovation in future technological development trends, such as identifying new products, services, or markets that it may give rise to in the next five or ten years; identifying the possibility of industry changes or the establishment of new standards triggered by the core innovation, such as determining whether it has the potential to become a new industry paradigm or drive the evolution of technical standards; and analyzing the impact of the core innovation on the existing ecosystem, such as assessing its potential impact on existing technology stacks, development tools, user habits, or business models.Therefore, based on the results of forward-looking analysis, the long-term potential value of improvement suggestions or solutions is quantified. The aim is to provide a more comprehensive and strategically significant evaluation indicator to avoid underestimating their long-term value due to short-term conflicts. The quantification result can be a comprehensive score or a combination of assessments from multiple dimensions (such as disruptiveness index, future market potential, ecosystem adaptability, etc.).

[0069] Optional steps for analyzing the impact of core innovations on the existing ecosystem include: To understand the impact of the core innovative points in the candidate's thought process on the existing ecosystem; Decompose the impact and identify the specific changes in the impact at the level of technical components, data flow, interface specifications, or development practices. Continuously track the evolution of specific changes in the project code repository, technical documentation, or community discussions; Based on evolution records, identify the local impact of specific changes on the technology ecosystem at different points in time; By accumulating local impacts and combining them with their duration, breadth, and guiding role in subsequent technological development, the long-term cumulative impact can be quantified.

[0070] Specifically, understanding the impact of a candidate's core innovative ideas on the existing ecosystem refers to collecting raw information about the potential influence of that innovation on the current technological environment, architecture, or workflows. This information can come from discussions on online platforms, proposed design solutions, and explanations in code submissions.

[0071] This approach involves decomposing the impact to identify specific changes at the levels of technical components, data flow, interface specifications, or development practices. The aim is to break down the macro-level impact into observable and quantifiable details. For example, at the technical component level, this might involve replacing existing libraries or frameworks or introducing new modules; at the data flow level, it might involve changes to data transmission protocols or adjustments to data formats; at the interface specification level, it might involve modifications to API definitions or updates to inter-service communication methods; and at the development practice level, it might involve adopting new development toolchains, testing methods, or deployment strategies. This decomposition allows for more precise pinpointing of the impact points.

[0072] Furthermore, continuously tracking the evolution of specific changes in project code repositories, technical documentation, or community discussions refers to real-time or periodic monitoring of relevant project code repositories, technical documentation, and technical community discussions through automated tools or manual review. The aim is to capture all relevant information throughout the entire lifecycle of these specific changes, from proposal to implementation and feedback, including the timing of the change, participants, discussion content, and issues resolved.

[0073] Therefore, identifying the local impact of specific changes on the technology ecosystem at different points in time based on evolution records refers to analyzing the direct and limited impact of each specific change on the technology ecosystem within a specific time period after its introduction or implementation, based on the tracked evolution records. For example, a change in an interface specification may require modules that depend on that interface to make adaptation modifications in the short term; this is a local impact.

[0074] Ultimately, the cumulative impact of localized influences is calculated by combining their duration, breadth, and guiding role in subsequent technological development. This quantifies the long-term cumulative impact. This means integrating all identified localized influences and comprehensively considering the duration (i.e., the length of time the influence exists or is in effect), breadth (i.e., the scope of affected technological components, teams, or projects), and potential guiding role in future technological development. Through this cumulative and comprehensive evaluation, a quantitative indicator can be obtained to measure the long-term, profound impact of core innovations on the entire technological ecosystem.

[0075] Optionally, the steps to quantify the long-term cumulative impact by accumulating the local effects and combining them with the corresponding duration, breadth of impact, and guiding role in subsequent technological development include: Obtain information on the impact of the core innovative points in the candidate's thought process on the existing ecosystem; impact information includes commit history from the project code repository, revision history of technical documents, and interaction records of community discussions; Content analysis of submission records, revision history, and interaction records is performed to extract descriptions of the duration, breadth of impact, and guiding role of core innovations in subsequent technological development; The extracted descriptions are standardized to unify their expression format and units of measurement; Identify whether there are inconsistencies or conflicts among the standardized descriptions. Inconsistencies or conflicts include: overlapping time ranges but different numerical values ​​in the descriptions of duration from different sources; overlapping ranges but not completely consistent coverage of the objects in the descriptions of the breadth of influence from different sources; and opposite directions or differences in degree between the descriptions of the guiding effect from different sources that exceed a threshold. When inconsistencies or conflicts are identified, they are resolved according to preset priority rules. These priority rules include: the commit history of the project code repository has the highest priority; the revision history of technical documents has the second highest priority; and the interaction records of community discussions have the lowest priority. Based on the deconstructed description, the duration, scope of impact, and guiding role in subsequent technological development are quantified. By accumulating the quantified duration, breadth of influence, and guiding effect, we can obtain the long-term cumulative impact.

[0076] "Impact Information" refers to the raw data set showing the impact of the core innovative points in the applicant's thought process on the existing technology ecosystem. Specifically, "Project Code Repository Commit History" can include records of code additions, deletions, modifications, queries, branch merging, and version releases, directly reflecting specific changes and evolutions at the technical implementation level. "Technical Documentation Revision History" covers updates and version iterations of design documents, API specifications, user manuals, architecture diagrams, etc., reflecting the evolution of design concepts, standards, and knowledge accumulation. "Community Discussion Interaction Records" include forum posts, mailing lists, discussions in instant messaging groups, comments, likes, and adoption suggestions, reflecting the technical community's attention, feedback, discussion, and potential adoption intentions regarding the innovative point.

[0077] "Content analysis" refers to using Natural Language Processing (NLP) techniques, code analysis tools, structured data extraction methods, or manual review to identify and extract key information from raw unstructured or semi-structured data. For example, extracting feature release dates (as a reference for duration), the number of affected files or modules (as a reference for the breadth of impact), or future architecture projections (as a guiding reference) from commit comments; identifying release dates, affected module ranges, or advocacy for new development models from document revision history; and analyzing discussion popularity, number of participants, the spread of discussion topics, and suggestions or questions regarding future technical directions from community discussions.

[0078] "Standardization" aims to eliminate differences in expression and units of measurement among data from different sources, ensuring the accuracy of subsequent comparisons and quantifications. For example, it standardizes time descriptions from different sources into date formats or durations in days; it standardizes the breadth of impact into the number of affected modules, lines of code, or function points; and it transforms descriptions of guiding effects into standardized scoring, classification systems, or impact levels.

[0079] "Inconsistency or conflict" refers to contradictions or significant differences in the same type of description (such as duration, breadth of impact, or guiding role) from different data sources after standardization. For example, code commit history shows a feature was completed on date A, while technical documentation revision history records it as date B, constituting a conflict in duration; community discussions suggest an innovation impacted the entire system, while code analysis shows it only affected a few modules, constituting a conflict in breadth of impact; or community discussions hold a positive guiding attitude towards an innovation, while technical documentation points out potential risks, constituting a conflict in guiding role. These situations need to be identified.

[0080] Priority rules are a key mechanism for resolving conflicts. Typically, "project code repository commit history," which directly reflects technical implementation and evolution, is considered the most authoritative and direct evidence, and therefore receives the highest priority. Next is the "revision history of technical documentation," which represents official or semi-official design and specification records and has high credibility. While "community discussion interaction records" reflect broad viewpoints and early feedback, their information may not be rigorously verified and may contain subjective opinions, thus receiving the lowest priority. This rule allows for the selection of the most reliable information as the final quantitative basis when conflicts arise, thereby improving the objectivity of the evaluation.

[0081] "Quantification" refers to the use of a unified and reliable description to quantify various indicators after conflict resolution. For example, duration can be directly calculated in days or months; the breadth of impact can be counted by the number of affected modules or files, or an impact index can be obtained through complexity analysis; the guiding role can be scored through expert ratings, semantic analysis models, or pre-set quantitative models, such as a score from 1 to 10, or divided into "weak guidance," "medium guidance," "strong guidance," etc.

[0082] "Accumulation" refers to the comprehensive calculation of the quantified duration, breadth of influence, and guiding role to form a comprehensive assessment value that can fully reflect the long-term cumulative impact of core innovations. This can be a weighted average, a multi-dimensional scoring model, or a machine learning-based predictive model, aiming to integrate the impact of different dimensions into a unified and comparable indicator to facilitate a comprehensive assessment of the applicant's abilities.

[0083] As a specific implementation, suppose a job applicant proposes a core innovation regarding microservice architecture optimization on an online platform. When assessing its long-term cumulative impact on the existing ecosystem, the system first obtains impact information. For example, the project's code repository commit history shows that the code refactoring related to this innovation began on January 1, 2023, and was completed on March 1, 2023, affecting five core microservice modules. However, the technical documentation revision history records that the optimization solution was only officially released on February 1, 2023, claiming to affect eight microservice modules. Meanwhile, in community discussions, a user proposed a similar solution as early as January 15, 2023, sparking widespread discussion about its performance improvements to the entire system, but some discussions also pointed out that the solution might introduce new operational complexities.

[0084] At this point, the system will identify these inconsistencies or conflicts: discrepancies in duration between code and documentation records; in terms of impact scope, inconsistencies in the number of modules covered by code and documentation records; and regarding guidance, community discussions contain both positive feedback and potential risk warnings. Based on preset priority rules, commit records from the project code repository have the highest priority. Therefore, the system will adopt information from the code records regarding duration (January 1st to March 1st, 2023, i.e., 60 days) and impact scope (5 core microservice modules). For guidance, since community discussions have the lowest priority, the system will combine more authoritative descriptions from the code and documentation, and weight the risk warnings from community discussions to more comprehensively quantify its guidance effect. For example, it might quantify it with a comprehensive score (e.g., 8 points, considering both positive impact and potential risks).

[0085] Ultimately, by accumulating these quantitative values, a more accurate and objective long-term cumulative impact assessment result is obtained, avoiding assessment bias caused by simple averaging or ignoring conflicts.

[0086] This application also discloses a big data analysis and mining system for talent recruitment, used to perform big data analysis and mining for talent recruitment, combined with... Figure 3 As shown, the big data analysis and mining system 1 for talent recruitment includes: The text information acquisition module 11 is used to acquire the text information corresponding to the applicant on the online platform; The text data forming module 12 is used to organize text information and form corresponding text data; The context analysis execution module 13 is used to perform context analysis on critical expressions in text data. Context analysis includes: identifying whether the object targeted by the critical expression is a technical concept, obtaining object identification results; determining whether the intent of the critical expression is to solve a problem or propose an improvement, obtaining intent judgment results; identifying whether the critical expression contains solutions or improvement suggestions, obtaining solution suggestion identification results; analyzing the logical structure of the critical expression, obtaining logical analysis results; and organizing the object identification results, intent judgment results, solution suggestion identification results, and logical analysis results into corresponding context analysis results. The value determination execution module 14 is used to determine the value of critical expressions based on the context analysis results, and obtain the value determination results; The competency-based association module 15 is used to associate the value determination result, which is a valuable critical expression, with the competency characteristics of the applicant. The risk warning labeling module 16 is used to mark critical expressions that have no value as potential risk warnings. The assessment information fusion module 17 is used to integrate competency characteristics with assessment information from other sources for the applicant; The integrated information processing module 18 is used to adjust the proportion of different assessment information in the integration process according to the specific requirements of the target position, and to process the traditional career path information to obtain the corresponding ability characteristic assessment integration information of the applicant.

[0087] Specifically, the text information acquisition module is configured to acquire text information generated or published by job applicants on online platforms. This module can be implemented in various ways, such as by integrating with application programming interfaces (APIs) provided by various online platforms to automatically acquire publicly available text data from applicants, provided appropriate authorization is obtained; or by receiving personal homepage links, user identifiers, or account information proactively submitted by applicants to selectively extract text content from the corresponding platforms. Through these methods, the text information acquisition module can continuously and stably acquire raw text information reflecting applicants' technical viewpoints and discussion behaviors, providing a data source for subsequent analysis.

[0088] The text data formation module is used to organize the acquired text information into structured text data. During processing, this module performs data cleaning, noise filtering, format normalization, and semantic unit decomposition on the raw text information, transforming the unstructured text content into a data structure that is easy to analyze. The text data formation module can be implemented as a standalone data preprocessing service, deployed on a dedicated server or running as a functional component in a microservice architecture. To adapt to large-scale text processing scenarios, this module can employ a parallel computing framework to batch process text data, thereby improving data processing efficiency and ensuring system scalability.

[0089] The context analysis execution module performs contextual analysis on critical expressions contained in text data. Based on the structured text data output by the text data formation module, this module identifies the technical objects, expressive intentions, and logical structures involved in the critical expressions, thereby avoiding superficial or emotional judgments about the text content. The context analysis execution module can be implemented as an analysis service integrating multiple natural language processing models, such as entity recognition models for identifying technical concepts, classification models for determining expressive intentions, and logical analysis models for assessing the structural integrity of arguments. Through collaborative processing of multidimensional analysis results, the context analysis execution module can output contextual analysis results that reflect the technical attributes and constructiveness of the critical expressions.

[0090] The value determination execution module is used to assess the value of critical expressions based on contextual analysis results, yielding a value determination result. This module comprehensively evaluates multiple dimensions of information from the contextual analysis results to determine whether the critical expression has a clear technical focus, improvement purpose, and executable argumentation logic. The value determination execution module can be implemented as a decision support unit, internally containing either a rule-based evaluation mechanism or a trained machine learning model for scoring and classifying different types of contextual analysis results. Through this module's processing, the system can distinguish between valuable and worthless critical expressions, providing a basis for subsequent capability mapping and risk warnings.

[0091] The competency feature association module is used to associate valuable critical expressions identified through value assessment with the competency features of job applicants. This module performs semantic matching on the technical fields, problem types, and solution approaches involved in high-value critical expressions, mapping them to a predefined competency feature tagging system, thereby dynamically updating the applicant's competency profile. The competency feature association module can be implemented as a competency knowledge graph construction and update service, enabling job applicants' competency features to no longer rely solely on static resume information, but to reflect their depth of thinking and technical contributions in real-world technical communication scenarios.

[0092] The risk warning labeling module is used to mark critical expressions that are deemed worthless, creating potential risk warnings. This module categorizes low-value critical expressions based on preset risk rules or abnormal behavior identification models and generates corresponding risk label information. These risk labels do not directly negate the applicant's abilities but are stored as supplementary assessment information in the applicant's risk profile, indicating factors that may require further attention in areas such as communication style, emotional expression, or technical judgment.

[0093] The assessment information fusion module integrates the competency characteristics derived from text analysis with assessment information from other sources. This other assessment information may include resume analysis results, interview evaluation data, test scores, and project experience. By uniformly modeling data from different sources and dimensions, the assessment information fusion module enables the comprehensive calculation of multi-source assessment information within the same evaluation space, resulting in a more comprehensive candidate assessment.

[0094] The integrated information processing module adjusts the weights of various assessment information during the integration process based on the specific requirements of the target position, and reprocesses traditional career path information to obtain integrated assessment information of the applicant's corresponding competency characteristics. This module can allocate differentiated weights to different evaluation dimensions such as innovation ability, technical depth, and stability based on job tags or job competency requirement models. Furthermore, for traditional career path information, this module analyzes the applicant's competency growth trajectory and technical accumulation process, rather than simply judging based on years of work experience or frequency of employment, thereby achieving de-biased assessment of applicants from non-traditional professional backgrounds.

[0095] This application proposes a big data analysis and mining system for talent recruitment. Through data transfer and collaborative processing among the aforementioned modules, it constructs a competency assessment system centered on real technical behaviors. Compared to existing methods that focus on static resumes or simple sentiment analysis, this system can deeply understand applicants' critical expressions and interactive behaviors in professional contexts, thereby forming a more objective and comprehensive competency profile. This significantly improves the accuracy of identifying innovative and high-potential talents and the fairness of the assessment.

[0096] The above are merely embodiments of this application and are not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A big data analysis and mining method for talent recruitment, characterized in that, The method comprises the following steps: obtaining the text information of the candidate on the network platform; organizing the text information to form corresponding text data; performing context analysis on the critical expression in the text data; the context analysis comprises: identifying whether the object of the critical expression is a technical concept to obtain an object identification result; judging whether the intention of the critical expression is to solve a problem or to propose an improvement to obtain an intention judgment result; identifying whether the critical expression contains a solution or an improvement suggestion to obtain a solution suggestion identification result; analyzing the logical structure of the critical expression to obtain a logical analysis result; and organizing the object identification result, the intention judgment result, the solution suggestion identification result and the logical analysis result into a corresponding context analysis result; determining the value of the critical expression according to the context analysis result to obtain a value determination result; associating the critical expression with the value determination result of the critical expression with the value to the ability characteristics of the candidate; marking the critical expression with the value determination result of the critical expression without value as a potential risk prompt; fusing the ability characteristics with the evaluation information of the candidate from other sources; adjusting the proportion of different evaluation information in the fusion process according to the specific requirements of the target post, and processing the traditional career path information to obtain the ability characteristic evaluation fusion information of the candidate.

2. The method of claim 1, wherein the method is characterized by, The step of performing context analysis on the critical expression in the text data comprises: obtaining the interaction records of the candidate on the network platform; organizing the interaction records to form corresponding interaction data; identifying the relationship between the interaction records in the interaction data; constructing the thinking path of the candidate according to the relationship between the interaction records; analyzing the response behavior triggered by the thinking path in the community; evaluating the constructiveness of the thinking path according to the response behavior to obtain a constructiveness evaluation result; associating the thinking path with the value determination result of the thinking path with the value to the ability characteristics of the candidate; marking the thinking path with the value determination result of the thinking path without value as a potential risk prompt. 3.The method of claim 2, wherein, The step of analyzing the response behavior triggered by the thinking path in the community comprises: obtaining professional field range information of the technical issues involved in the thinking path; obtaining community activity information of the technical issues involved in the thinking path; judging whether to start a low-activity compensation mechanism according to the professional field range information and the community activity information; when the low-activity compensation mechanism is started, the evaluation weight of the number of response behaviors is reduced; identifying and quantifying the response behaviors that reflect professional recognition, deep thinking or substantial impact; tracking whether the thinking path is indirectly adopted by other related technical discussions or projects to obtain tracking reference information; after reducing the evaluation weight of the number of response behaviors and identifying and quantifying the response behaviors that reflect professional recognition, deep thinking or substantial impact, analyzing and evaluating the response behavior triggered by the thinking path in the community based on the tracking reference information.

4. The method of claim 3, wherein the method is characterized by, The step of tracking whether the thinking path is indirectly adopted by other related technical discussions or projects to obtain tracking reference information comprises: continuously monitoring the submission records and version control history of the project code repository; Based on the monitored submission records and version control history, identify the code refactoring, new function implementation or design pattern change related to the innovative ideas in the candidate's thinking path; Analyze the correspondence between the code refactoring, new function implementation or design pattern change and the innovative ideas proposed in the candidate's thinking path; Based on the correspondence, evaluate the substantive impact of the implicit adoption according to the size, complexity and impact on key modules of the project, and obtain tracking reference information.

5. The method of claim 3, wherein the method further comprises: The step of determining whether to start the low-activity compensation mechanism based on the professional field range information and the community activity information includes: When receiving the interaction records of the candidate on the network platform, real-time acquisition of the professional field range information and the community activity information of the technical topics involved in the interaction records; According to the professional field range information and the community activity information, combined with the preset judgment rule, judge whether to start the low-activity compensation mechanism; When it is judged that the low-activity compensation mechanism needs to be started, record the current timestamp, and start a compensation mechanism effective timer; During the counting period of the compensation mechanism effective timer, continuously monitor the changes of the professional field range information and the community activity information; When it is monitored that the professional field range information or the community activity information changes beyond the threshold, resulting in not meeting the condition for starting the low-activity compensation mechanism, terminate the low-activity compensation mechanism, and record the termination timestamp.

6. The method of claim 3, wherein the method further comprises: The step of identifying and quantifying the response behavior reflecting professional recognition, deep thinking or substantial impact includes: Obtain the interaction records of the candidate on the network platform; Content type identification is performed on the interaction records to distinguish between text-based interaction content and non-text-based interaction content; When non-text-based interaction content is identified, data conversion is performed on the non-text-based interaction content to convert it into various structured data; the data conversion process includes: extracting comments in code submission to identify technical discussions, problem feedback or design intentions contained; analyzing annotations in design drawings to identify design optimization suggestions, function improvement points or technical questions contained; performing semantic analysis on the transcription of oral discussion recordings to identify professional opinions, argumentation processes or solutions contained; Perform content analysis on the various structured data after data conversion to identify whether they contain detailed argumentation of technical solutions, in-depth analysis of existing problems or prediction of future development trends, and obtain response behavior identification results of the content type; Identify whether the various structured data after data conversion reference authoritative literature, industry standards or frontier research achievements, and obtain response behavior identification results of the reference type; Quantify the innovativeness, feasibility and potential impact on the technical community of the improvement suggestions or solutions proposed in the various structured data after data conversion.

7. The method of claim 6, wherein the method further comprises: The step of quantifying the innovativeness, feasibility and potential impact on the technical community of the improvement suggestions or solutions proposed in the various structured data after data conversion includes: Obtain the improvement suggestions or solutions proposed by the candidate; performing semantic analysis on the improvement suggestion or solution to extract corresponding core innovation points; comparing the core innovation points with mainstream technical paradigms in the current technical field to obtain a comparison result; based on the comparison result, identifying whether there is a conflict in function, structure or principle of the core innovation points; when the identified conflict reaches a preset threshold, starting a long-term value evaluation mode; in the long-term value evaluation mode, performing forward-looking analysis on the improvement suggestion or solution to obtain a forward-looking analysis result; the forward-looking analysis includes: evaluating the maturity of the underlying technology or theory relied on by the core innovation points; predicting potential application scenarios of the core innovation points in future technical development trends; identifying the possibility of industry changes or new standard establishment caused by the core innovation points; analyzing the impact of the core innovation points on the existing ecosystem; quantifying the long-term potential value of the improvement suggestion or solution according to the forward-looking analysis result.

8. The method of claim 7, wherein the method is characterized by, the step of analyzing the impact of the core innovation points on the existing ecosystem includes: obtaining the impact of the core innovation points on the existing ecosystem in the thinking path of the job applicant; decomposing the impact to identify specific changes in technical components, data flow, interface specification or development practice level; continuously tracking the evolution records of the specific changes in project code repository, technical documents or community discussions; based on the evolution records, identifying the local influence of the specific changes on the technical ecosystem at different time points; cumulatively quantifying the long-term cumulative influence by combining the corresponding duration, influence breadth and guiding role on subsequent technical development. 9.The method of claim 8, wherein, the step of cumulatively quantifying the long-term cumulative influence by combining the corresponding duration, influence breadth and guiding role on subsequent technical development includes: obtaining impact information of the core innovation points on the existing ecosystem in the thinking path of the job applicant; the impact information includes submission records from project code repository, revision history of technical documents and interaction records of community discussions; performing content analysis on the submission records, revision history and interaction records to extract descriptions about the duration, influence breadth and guiding role of the core innovation points on subsequent technical development; standardizing the extracted descriptions to unify the expression form and unit of measurement; identifying whether there is inconsistency or conflict between the standardized descriptions, the inconsistency or conflict including: different sources have different values in the time range overlap of the duration description; different sources have different coverage objects in the range intersection of the influence breadth description; different sources have opposite directions or degree differences exceeding the threshold in the guiding role description; when inconsistencies or conflicts are identified, resolving the inconsistencies or conflicts according to preset priority rules, the priority rules including: the submission records of the project code repository have the highest priority; the revision history of the technical documents has the second highest priority; the interaction records of the community discussions have the lowest priority; Based on the deconstructed description, the duration, scope of impact, and guiding role in subsequent technological development are quantified. By accumulating the quantified duration, breadth of influence, and guiding effect, we can obtain the long-term cumulative impact.

10. A big data analysis and mining system for talent recruitment, configured to perform big data analysis and mining for talent recruitment, characterized in that, include: The text information acquisition module is used to acquire the text information corresponding to the applicants on the online platform; The text data forming module is used to organize the text information and form corresponding text data; The context analysis execution module is used to perform context analysis on critical expressions in the text data. The context analysis includes: identifying whether the object targeted by the critical expression is a technical concept, obtaining an object identification result; determining whether the intent of the critical expression is to solve a problem or propose an improvement, obtaining an intent judgment result; identifying whether the critical expression contains a solution or improvement suggestion, obtaining a solution suggestion identification result; analyzing the logical structure of the critical expression, obtaining a logical analysis result; and organizing the object identification result, intent judgment result, solution suggestion identification result, and logical analysis result into a corresponding context analysis result. The value determination execution module is used to determine the value of critical expressions based on the context analysis results, and obtain the value determination result; The competency-based association module is used to associate the value assessment results, which are valuable critical expressions, with the competency characteristics of the applicant. The risk warning labeling module is used to mark critical expressions that are deemed to have no value as potential risk warnings. An assessment information fusion module is used to fuse the aforementioned competency characteristics with assessment information from other sources for the applicant; The integrated information processing module is used to adjust the weight of different assessment information during the integration process according to the specific requirements of the target position, and to process traditional career path information to obtain the corresponding integrated assessment information of the applicant's ability characteristics.

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