Deep learning-based employee performance analysis and training course matching system and method

Abstract

This invention relates to the field of computer software technology, specifically to a deep learning-based employee performance analysis and training course matching system and method. The system uses a multi-source data acquisition module to uniformly collect and standardize internal and external data to construct a complete employee dataset. An employee competency profile construction module extracts and fuses multimodal features from the data to generate dynamically updated multidimensional competency profiles. A performance weakness analysis engine analyzes the relationship between competency and performance, identifying weaknesses and strengths. A dynamic learning path planning module adjusts subsequent courses based on learning outcomes for continuous optimization. A training effectiveness evaluation and feedback module quantifies the training improvement effect and provides feedback to the system. A high-potential talent discovery and experience graph module constructs a reusable experience knowledge graph. An adaptive performance-competency causal evolution modeling module analyzes the causal relationship between competency and performance, dynamically adjusting competency weights and recommendation strategies.

Description

Technical Field

[0001] This invention relates to the field of computer software technology, specifically to a deep learning-based employee performance analysis and training course matching system and method. Background Technology

[0002] In today's highly competitive business environment, the continuous improvement of employee capabilities is crucial for companies to maintain their core competitiveness. Companies generally invest heavily in employee training, but traditional training models suffer from the following significant problems: I. Disconnect between performance evaluation and training needs. Traditional performance evaluations often result in a final score or grade, but fail to delve into the underlying skill gaps behind the performance results. For example, a salesperson's poor performance may stem from multiple factors such as communication skills, product knowledge, or time management. Traditional evaluations cannot accurately pinpoint the root cause of the problem, leading to a lack of targeted training in the future.

[0003] Second, avoid a "one-size-fits-all" approach to training courses. Companies often provide the same training courses to all employees or let them choose for themselves. The former fails to meet individual needs and is inefficient; the latter leads to low return on investment in training because employees lack a clear understanding of their own skill gaps and make blind choices.

[0004] Third, there is a lack of dynamic and continuous learning path planning. Employee growth is a dynamic process, and most current training is a one-off event, which fails to dynamically adjust and plan subsequent learning paths based on employees' learning progress, performance changes, and career development goals.

[0005] Fourth, the effectiveness of training is difficult to quantify and evaluate. After the training, it is difficult to quantify and evaluate the actual contribution of the training to the improvement of employees' subsequent performance, and it is impossible to form a closed-loop optimization mechanism of "evaluation-training-re-evaluation".

[0006] Fifth, insufficient mining of tacit knowledge. The success experiences and competency models of high-performing employees are difficult to systematically mine and refine, and cannot be effectively used to guide and train other employees.

[0007] Therefore, the industry urgently needs an intelligent employee training solution that can break down the barriers between performance evaluation and training development, and achieve precise, personalized, and dynamically optimized training. Summary of the Invention

[0008] To achieve the above-mentioned objectives, the present invention provides the following technical solution: a deep learning-based employee performance analysis and training course matching system, comprising the following modules: The multi-source data acquisition module is used to connect to internal and external data sources of the enterprise through interfaces to collect static and dynamic data of employees, and to clean, convert and encode the collected data to build a standardized dataset. The employee competency profile building module is used to extract and fuse features from the standardized dataset, transforming the employees' structured data, text data, and behavioral data into a unified multidimensional competency vector to form a dynamically updated employee competency profile. The performance weakness analysis engine is used to analyze the contribution of each capability dimension based on the mapping relationship between the employee capability profile and performance results, identify the employee's capability weaknesses and strengths, and generate corresponding interpretable analysis results. The course intelligent matching engine is used to retrieve candidate courses from a pre-built course knowledge graph based on the aforementioned skill gaps and employee skill profiles. It also combines job requirements, learning history, and learning preferences to perform multi-dimensional matching and ranking of candidate courses, generating personalized course recommendation results. The dynamic learning path planning module is used to track learning behavior data and learning effect data during the employee learning process, and dynamically adjust subsequent courses based on changes in ability and performance goals to form a continuously optimized learning path. The training effectiveness evaluation and feedback module is used to evaluate the training effectiveness by comparing changes in employee performance data and competency profiles before and after training, and to feed the evaluation results back to the employee competency profile building module and the course intelligent matching engine to achieve closed-loop optimization of the system. The High Potential Talent Discovery and Experience Graph module is used to identify the common ability characteristics of high-performing employees based on employee competency profiles and performance data, and to structure their experience to build a reusable knowledge graph. The Adaptive Performance-Competency Causal Evolution Modeling Module is used to model the causal relationship between changes in employee competence and changes in performance. It identifies the true contribution of competence improvement to performance changes through time series analysis and counterfactual reasoning, and dynamically adjusts the weights of each competence dimension to improve the accuracy of performance analysis and course matching.

[0009] Preferably, the multi-source data acquisition module further includes: The data access subunit is used to connect to the human resources management system, performance management system, office automation system, project management system, and enterprise learning platform through standard interfaces to uniformly collect static and dynamic data of employees. Static data includes personal information, job responsibilities, educational background, skills certificates, and historical training records, while dynamic data includes periodic performance evaluation results, 360-degree feedback, project completion and quality data, business behavior data, and learning behavior data. The data processing subunit is used to perform noise reduction, outlier removal, missing value completion, and time dimension alignment on the collected data. It also performs format conversion and standardization on data from different sources through unified encoding rules to build a unified dataset for subsequent modules to use.

[0010] Preferably, the employee competency profile building module further includes: The feature extraction subunit is used to decompose and normalize the structured data, perform semantic analysis on the text data to extract capability tag information, and perform sequence analysis on the behavior log data to extract the behavioral pattern features of employees. The vector modeling subunit is used to fuse the above-mentioned multi-source features, map different types of data to a unified feature space, construct a multi-dimensional capability representation model, and generate dynamically updatable employee capability profiles. At the same time, capability dimensions are classified and managed, including professional skills, general capabilities, work attitude, performance, and development potential.

[0011] Preferably, the performance weakness analysis engine further includes: The contribution calculation subunit is used to analyze the degree of influence of each capability dimension based on the mapping relationship between employee capability profile and performance results, calculate the positive and negative contributions of each capability dimension to performance results, and output the capability influence ranking results. The attribution explanation subunit is used to transform contribution results into readable analytical information, describe key competency factors affecting performance, and identify major weaknesses and core strengths to support subsequent training decisions.

[0012] Preferably, the course intelligent matching engine further includes: The course graph construction sub-unit is used to perform structured modeling of internal enterprise courses and external collaborative courses, and to establish the relationship between courses and competency tags, job requirements, course difficulty and prerequisites. The matching and sorting subunit is used to receive information on skill gaps, retrieve a set of courses related to the skill gaps in the course graph, and comprehensively evaluate and sort the candidate courses based on employee job requirements, historical learning records, learning preferences and course completion status, and output a personalized course recommendation list.

[0013] Preferably, the dynamic learning path planning module further includes a function to model the employee learning process as a continuous optimization process. By tracking the employee's learning progress, learning behavior and test results in real time, the module analyzes the trend of changes in the employee's ability. After the employee completes each course, the module updates the ability profile based on the learning effect and, in conjunction with the employee's current job requirements and the performance goals for the next stage, re-selects and adjusts subsequent courses, thereby forming a dynamically updated personalized learning path.

[0014] Preferably, the training effectiveness evaluation and feedback module further includes a function to evaluate the impact of training on employee performance improvement by comparing changes in employee performance data and competency profiles before and after training, to isolate the interference factors of external environmental changes on performance by constructing a comparative analysis mechanism, to calculate the actual improvement effect brought about by training, and to input the evaluation results as feedback information into the employee competency profile construction module and the course intelligent matching engine for optimizing subsequent competency analysis and course recommendation strategies.

[0015] Preferably, the high-potential talent discovery and experience graph module further includes the following: based on employee competency profiles and performance data, it identifies common competency characteristics of consistently high-performing employees through group analysis methods, constructs a high-potential talent identification model, and simultaneously extracts and structures information from project summaries, experience documents, and problem-solving records of high-performing employees to construct an experience knowledge graph, establishing associations between experience content and competency tags, business scenarios, and problem types to support experience sharing and knowledge reuse.

[0016] Preferably, the adaptive performance-capability causal evolution modeling module further includes: The time series modeling subunit is used to build a time series relationship model based on employees' historical competency profiles and performance data, and to analyze the temporal impact of competency changes on performance changes. The counterfactual reasoning subunit is used to construct a comparative path based on the actual path of employee capability change, and to simulate and predict performance changes in the absence of capability improvement, so as to assess the real impact of capability change. The weighting correction subunit is used to dynamically adjust the impact weights of the ability dimension based on the causal analysis results, and feeds the adjustment results back to the performance weakness analysis engine and the course intelligent matching engine to improve the accuracy of the overall system analysis and recommendation.

[0017] This invention also provides a method for employee performance analysis and training course matching based on deep learning, comprising the following steps: Step S1: Collect static and dynamic data of employees through a multi-source data acquisition module, and clean, align and integrate the data to form a standardized dataset; Step S2: Extract and fuse features from the standardized dataset using the employee competency profile building module to generate a multi-dimensional employee competency profile. Step S3: Analyze employee competency profiles and performance results using the performance weakness analysis engine, calculate the contribution of each competency dimension, identify competency weaknesses and strengths, and generate interpretable analysis results. Step S4: Based on skill gaps and employee skill profiles, the course intelligent matching engine retrieves candidate courses from the course knowledge graph, matches and sorts them, and generates personalized course recommendation results. Step S5: Track the employee's learning process through the dynamic learning path planning module and dynamically adjust the learning path according to changes in learning effectiveness and ability; Step S6: Evaluate the performance changes before and after training through the training effectiveness evaluation and feedback module, and feed the evaluation results back to each module of the system to achieve closed-loop optimization; Step S7: Identify the characteristics of high-performing employees and construct a knowledge graph through the high-potential talent discovery and experience graph module to achieve experience reuse; Step S8: Perform causal analysis on the changes in ability and performance through the adaptive performance-ability causal evolution modeling module, and dynamically adjust the ability weights and course recommendation strategies based on the analysis results.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows: First, training is made more precise, shifting from "broad-based" to "precision-based," ensuring that training resources are truly invested in the areas where employees need the most improvement, thus greatly increasing training efficiency and return on investment.

[0019] Second, the performance improvement visualization clearly shows how skill gaps affect performance, and how training can fill those gaps and ultimately improve performance, allowing both employees and managers to see the "cause and effect chain" of growth.

[0020] Third, personalized learning paths are created for each employee, providing a dynamic learning and development path tailored to their individual needs. This transforms employee development from discrete events into a continuous and predictable growth process, which is beneficial for employee career planning and the building of the company's talent pipeline.

[0021] Fourth, experience can be replicated. By mining the characteristics of high-potential talents and building a best practice knowledge graph, tacit knowledge is made explicit and structured, so that successful experiences can be replicated and passed on on a large scale within the organization.

[0022] Fifth, scientific decision-making provides strong data support and quantitative evaluation for corporate training decisions, course procurement, and talent development plans, enabling human resource management to shift from experience-driven to data-driven intelligence. Attached Figure Description

[0023] Figure 1 A schematic diagram of the system modules provided in this application; Figure 2 A flowchart illustrating the method steps provided in this application. Detailed Implementation

[0024] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0025] refer to Figure 1 This invention provides a deep learning-based employee performance analysis and training course matching system, which includes the following modules: The multi-source data acquisition module is used to connect to internal and external data sources of the enterprise through interfaces to collect static and dynamic data of employees, and to clean, convert and encode the collected data to build a standardized dataset. The employee competency profile building module is used to extract and fuse features from the standardized dataset, transforming the employees' structured data, text data, and behavioral data into a unified multidimensional competency vector to form a dynamically updated employee competency profile. The performance weakness analysis engine is used to analyze the contribution of each capability dimension based on the mapping relationship between the employee capability profile and performance results, identify the employee's capability weaknesses and strengths, and generate corresponding interpretable analysis results. The course intelligent matching engine is used to retrieve candidate courses from a pre-built course knowledge graph based on the aforementioned skill gaps and employee skill profiles. It also combines job requirements, learning history, and learning preferences to perform multi-dimensional matching and ranking of candidate courses, generating personalized course recommendation results. The dynamic learning path planning module is used to track learning behavior data and learning effect data during the employee learning process, and dynamically adjust subsequent courses based on changes in ability and performance goals to form a continuously optimized learning path. The training effectiveness evaluation and feedback module is used to evaluate the training effectiveness by comparing changes in employee performance data and competency profiles before and after training, and to feed the evaluation results back to the employee competency profile building module and the course intelligent matching engine to achieve closed-loop optimization of the system. The High Potential Talent Discovery and Experience Graph module is used to identify the common ability characteristics of high-performing employees based on employee competency profiles and performance data, and to structure their experience to build a reusable knowledge graph. The Adaptive Performance-Competency Causal Evolution Modeling Module is used to model the causal relationship between changes in employee competence and changes in performance. It identifies the true contribution of competence improvement to performance changes through time series analysis and counterfactual reasoning, and dynamically adjusts the weights of each competence dimension to improve the accuracy of performance analysis and course matching.

[0026] The multi-source data acquisition module is deployed in the enterprise data platform layer and interfaces with the enterprise's existing information systems through a unified interface. Specifically, this module first connects to the human resource management system, performance management system, office automation system, project management system, and enterprise learning platform through the data access subunit to achieve centralized collection of multi-source employee data. Among them, the human resource management system provides basic employee information data, including job position, education, and certificate information; the performance management system provides periodic performance appraisal results; the project management system provides data on employee participation in project execution and quality assessment; and the learning platform provides behavioral data such as course learning progress and test scores.

[0027] During the data collection process, the data formats of different systems differ. For example, some systems store data in structured tables, while others record data in log or text format. To address this, the data processing subunit performs unified processing on the collected data, including filtering out abnormal data, completing missing fields, converting timestamps to a unified format, and performing unified encoding processing according to preset rules. Through the above processing, the multi-source heterogeneous data is finally integrated into a dataset with a unified structure, providing a reliable data foundation for the subsequent construction of capability profiles.

[0028] The employee competency profile building module processes the datasets output by the multi-source data acquisition module. First, it performs targeted processing on different types of data through the feature extraction sub-unit. For structured data, such as performance scores and project indicators, the indicators are decomposed and converted into standard features. For text data, such as project summaries and evaluation feedback, key competency tags are extracted through semantic analysis. For behavioral data, such as learning records and operation logs, employee behavioral pattern features are extracted through sequence analysis.

[0029] Subsequently, the extracted multi-source features are fused through a vector modeling subunit, mapping data from different sources to a unified feature space to form a multi-dimensional capability representation model. This model expresses employee capabilities in vector form and can be adjusted in real time according to data updates. In the process of capability representation, the system divides capabilities into multiple dimensions, including professional skills, general abilities, work attitude, performance, and development potential, thereby constructing a complete employee capability profile and providing basic support for subsequent analysis and recommendations.

[0030] The performance weakness analysis engine is used to analyze the relationship between employee capabilities and performance. First, the contribution calculation sub-unit analyzes the impact of each capability dimension based on the correlation between employee capability profile and performance results. By quantifying the role of different capability dimensions in performance results, the positive or negative contribution value of each capability to performance is obtained and sorted according to the degree of impact.

[0031] Subsequently, the attribution explanation subunit processes the calculation results, transforming the abstract contribution data into more readable analytical information. For example, for employees with lower performance, the system can clearly identify their main weaknesses and explain the specific impact of these weaknesses on performance; for employees with higher performance, it identifies their core strengths.

[0032] The course intelligent matching engine recommends courses based on employees' skill gaps. First, it constructs sub-units through a course graph to structurally model internal and external course resources, establishing a network of connections between courses and skill tags, job requirements, course difficulty, and prerequisites, thus forming a searchable knowledge structure among courses.

[0033] Based on this, the matching and ranking subunit receives the capability gap information output by the performance gap analysis engine, retrieves the set of courses related to the capability gap in the course graph, and then performs matching calculations on the candidate courses by combining the employee's job requirements, historical learning records and learning preferences. It also sorts the candidate courses by taking into account multiple factors, and finally outputs a personalized course recommendation list that meets the employee's development needs, thereby achieving accurate matching of training resources.

[0034] The dynamic learning path planning module is used to continuously optimize and manage the employee learning process. The system first regards the employee learning process as a continuous evolution process, tracks the employee's learning behavior data, course completion status and test results in real time, and analyzes the changing trend of employee capabilities based on this.

[0035] When an employee completes a course, the system updates their competency profile based on their learning outcomes. It then re-selects subsequent courses based on the employee's current job requirements and performance goals for the next stage. If an employee shows significant improvement in a particular competency dimension, the system will automatically recommend higher-level courses. If the learning outcomes are unsatisfactory, the system will adjust the difficulty or type of the courses. Through this dynamic adjustment mechanism, the system can create a continuously optimized learning path, thereby improving learning efficiency and training effectiveness.

[0036] The training effectiveness evaluation and feedback module is used to quantify the impact of training on employee performance. The system first compares the changes in employee performance data and competency profiles before and after training, and analyzes the degree of impact of training on competency improvement and performance changes.

[0037] To improve the accuracy of the assessment, the system constructs a comparative analysis mechanism to compare and analyze data before and after employee training. It also considers the impact of changes in the external environment on performance and reduces the influence of interfering factors through data processing methods. This allows for a more accurate assessment of the actual effects of the training. The assessment results are not only used to generate training effectiveness reports, but also serve as feedback information input into the employee competency profile building module and the intelligent course matching engine to optimize subsequent analysis and recommendation strategies, thereby achieving closed-loop optimization of the system.

[0038] The High Potential Talent Discovery and Experience Mapping module is used to identify outstanding talents and their experiences within an organization. The system first identifies consistently high-performing employees based on employee competency profiles and performance data through group analysis methods, extracts their common competency characteristics, and constructs a high-potential talent identification model to assist in talent assessment and development decisions.

[0039] Meanwhile, the system extracts information from the project summaries, experience documents, and problem-solving records of high-performing employees, transforming unstructured experience into structured data and constructing an experience knowledge graph. In this graph, experience content is associated with capability tags, business scenarios, and problem types, thereby achieving the systematic accumulation and sharing of experience. Other employees can obtain relevant experience based on this graph during the learning process, improving problem-solving efficiency.

[0040] The Adaptive Performance-Competency Causal Evolution Modeling Module is used to analyze the causal relationship between changes in employee competence and changes in performance. First, the Time Series Modeling Sub-unit constructs a time series relationship model based on the employee's historical competence profile and performance data to analyze the temporal relationship between competence changes and performance changes, thereby identifying the lagged impact of competence changes on performance.

[0041] Secondly, the counterfactual reasoning subunit constructs a comparison path based on the actual path of employee capability change. By simulating the performance change trend of employees without capability improvement, it compares it with the actual performance to assess the true impact of capability change.

[0042] Finally, the weight correction subunit dynamically adjusts the weight of the ability dimension in the performance analysis based on the causal analysis results, and feeds the adjustment results back to the performance weakness analysis engine and the course intelligent matching engine, so that the system shifts from simple correlation analysis to causal-driven decision-making, thereby improving the accuracy of overall analysis and recommendation.

[0043] refer to Figure 2 This invention provides a method for matching employee performance analysis and training courses based on deep learning, including the following steps: Step 1: Collect static and dynamic data of employees through a multi-source data acquisition module, and clean, align and integrate the data to form a standardized dataset; Step 2: Use the employee competency profile building module to extract and fuse features from the standardized dataset to generate multi-dimensional employee competency profiles; Step 3: Analyze employee competency profiles and performance results using the performance weakness analysis engine, calculate the contribution of each competency dimension, identify competency weaknesses and strengths, and generate interpretable analysis results. Step 4: Based on skill gaps and employee skill profiles, the course intelligent matching engine searches for candidate courses in the course knowledge graph, matches and sorts them, and generates personalized course recommendation results. Step 5: Track the employee learning process through the dynamic learning path planning module and dynamically adjust the learning path based on changes in learning outcomes and abilities; Step Six: Evaluate the performance changes before and after training through the training effectiveness evaluation and feedback module, and feed the evaluation results back to each module of the system to achieve closed-loop optimization; Step 7: Identify the characteristics of high-performing employees and construct a knowledge graph through the high-potential talent discovery and experience graph module to enable experience reuse; Step 8: Conduct causal analysis on the relationship between ability changes and performance changes using the adaptive performance-ability causal evolution modeling module, and dynamically adjust ability weights and course recommendation strategies based on the analysis results.

[0044] It should be noted that, unless otherwise specified, the embodiments and features and technical solutions in the present invention can be combined with each other.

[0045] Obviously, the embodiments described above are merely some embodiments of the present invention, not all embodiments. The accompanying drawings show preferred embodiments of the present invention, but do not limit the patent scope of the present invention. The present invention can be implemented in many different forms; rather, these embodiments are provided to provide a more thorough and complete understanding of the disclosure of the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the patent protection scope of this invention.

Claims

1. A deep learning-based intelligent matching system for employee performance analysis and training courses, characterized in that, Includes the following modules: The multi-source data acquisition module is used to connect to internal and external data sources of the enterprise through interfaces to collect static and dynamic data of employees, and to clean, convert and encode the collected data to build a standardized dataset. The employee competency profile building module is used to extract and fuse features from the standardized dataset, transforming the employees' structured data, text data, and behavioral data into a unified multidimensional competency vector to form a dynamically updated employee competency profile. The performance weakness analysis engine is used to analyze the contribution of each capability dimension based on the mapping relationship between the employee capability profile and performance results, identify the employee's capability weaknesses and strengths, and generate corresponding interpretable analysis results. The course intelligent matching engine is used to retrieve candidate courses from a pre-built course knowledge graph based on the aforementioned skill gaps and employee skill profiles. It also combines job requirements, learning history, and learning preferences to perform multi-dimensional matching and ranking of candidate courses, generating personalized course recommendation results. The dynamic learning path planning module is used to track learning behavior data and learning effect data during the employee learning process, and dynamically adjust subsequent courses based on changes in ability and performance goals to form a continuously optimized learning path. The training effectiveness evaluation and feedback module is used to evaluate the training effectiveness by comparing changes in employee performance data and competency profiles before and after training, and to feed the evaluation results back to the employee competency profile building module and the course intelligent matching engine to achieve closed-loop optimization of the system. The High Potential Talent Discovery and Experience Graph module is used to identify the common ability characteristics of high-performing employees based on employee competency profiles and performance data, and to structure their experience to build a reusable knowledge graph. The Adaptive Performance-Competency Causal Evolution Modeling Module is used to model the causal relationship between changes in employee competence and changes in performance. It identifies the true contribution of competence improvement to performance changes through time series analysis and counterfactual reasoning, and dynamically adjusts the weights of each competence dimension to improve the accuracy of performance analysis and course matching.

2. The intelligent matching system for employee performance analysis and training courses based on deep learning according to claim 1, characterized in that, The multi-source data acquisition module further includes: The data access subunit is used to connect to the human resources management system, performance management system, office automation system, project management system, and enterprise learning platform through standard interfaces to uniformly collect static and dynamic data of employees. Static data includes personal information, job responsibilities, educational background, skills certificates, and historical training records, while dynamic data includes periodic performance evaluation results, 360-degree feedback, project completion and quality data, business behavior data, and learning behavior data. The data processing subunit is used to perform noise reduction, outlier removal, missing value completion, and time dimension alignment on the collected data. It also performs format conversion and standardization on data from different sources through unified encoding rules to build a unified dataset for subsequent modules to use.

3. The intelligent matching system for employee performance analysis and training courses based on deep learning according to claim 1, characterized in that, The employee competency profile building module further includes: The feature extraction subunit is used to decompose and normalize the structured data, perform semantic analysis on the text data to extract capability tag information, and perform sequence analysis on the behavior log data to extract the behavioral pattern features of employees. The vector modeling subunit is used to fuse the above-mentioned multi-source features, map different types of data to a unified feature space, construct a multi-dimensional capability representation model, and generate dynamically updatable employee capability profiles. At the same time, capability dimensions are classified and managed, including professional skills, general capabilities, work attitude, performance, and development potential.

4. The intelligent matching system for employee performance analysis and training courses based on deep learning according to claim 1, characterized in that, The performance weakness analysis engine further includes: The contribution calculation subunit is used to analyze the degree of influence of each capability dimension based on the mapping relationship between employee capability profile and performance results, calculate the positive and negative contributions of each capability dimension to performance results, and output the capability influence ranking results. The attribution explanation subunit is used to transform contribution results into readable analytical information, describe key competency factors affecting performance, and identify major weaknesses and core strengths to support subsequent training decisions.

5. The intelligent matching system for employee performance analysis and training courses based on deep learning according to claim 1, characterized in that, The intelligent course matching engine further includes: The course graph construction sub-unit is used to perform structured modeling of internal enterprise courses and external collaborative courses, and to establish the relationship between courses and competency tags, job requirements, course difficulty and prerequisites. The matching and sorting subunit is used to receive information on skill gaps, retrieve a set of courses related to the skill gaps in the course graph, and comprehensively evaluate and sort the candidate courses based on employee job requirements, historical learning records, learning preferences and course completion status, and output a personalized course recommendation list.

6. The intelligent matching system for employee performance analysis and training courses based on deep learning according to claim 1, characterized in that, The dynamic learning path planning module further includes a function to model the employee learning process as a continuous optimization process. By tracking the employee's learning progress, learning behavior and test results in real time, the module analyzes the trend of changes in the employee's ability. After the employee completes each course, the module updates the ability profile based on the learning effect and, in conjunction with the employee's current job requirements and the performance goals for the next stage, re-selects and adjusts subsequent courses, thereby forming a dynamically updated personalized learning path.

7. The intelligent matching system for employee performance analysis and training courses based on deep learning according to claim 1, characterized in that, The training effectiveness evaluation and feedback module further includes a function to assess the impact of training on employee performance improvement by comparing changes in employee performance data and competency profiles before and after training. By constructing a comparative analysis mechanism, the module removes external environmental changes that interfere with performance, calculates the actual improvement effect brought about by training, and inputs the evaluation results as feedback information into the employee competency profile construction module and the course intelligent matching engine to optimize subsequent competency analysis and course recommendation strategies.

8. The intelligent matching system for employee performance analysis and training courses based on deep learning according to claim 1, characterized in that, The high-potential talent discovery and experience graph module further includes a method for identifying common ability characteristics of consistently high-performing employees based on employee competency profiles and performance data, using group analysis methods to construct a high-potential talent identification model. Simultaneously, it extracts and structures information from high-performing employees' project summaries, experience documents, and problem-solving records to construct an experience knowledge graph, establishing associations between experience content and competency tags, business scenarios, and problem types to support experience sharing and knowledge reuse.

9. The intelligent matching system for employee performance analysis and training courses based on deep learning according to claim 1, characterized in that, The adaptive performance-capability causal evolution modeling module further includes: The time series modeling subunit is used to build a time series relationship model based on employees' historical competency profiles and performance data, and to analyze the temporal impact of competency changes on performance changes. The counterfactual reasoning subunit is used to construct a comparative path based on the actual path of employee capability change, and to simulate and predict performance changes in the absence of capability improvement, so as to assess the real impact of capability change. The weighting correction subunit is used to dynamically adjust the impact weights of the ability dimension based on the causal analysis results, and feeds the adjustment results back to the performance weakness analysis engine and the course intelligent matching engine to improve the accuracy of the overall system analysis and recommendation.

10. A method for intelligent matching of employee performance analysis and training courses based on deep learning, characterized in that, The deep learning-based employee performance analysis and training course matching system described in any one of claims 1-9 includes the following steps: Step S1: Collect static and dynamic data of employees through a multi-source data acquisition module, and clean, align and integrate the data to form a standardized dataset; Step S2: Extract and fuse features from the standardized dataset using the employee competency profile building module to generate a multi-dimensional employee competency profile. Step S3: Analyze employee competency profiles and performance results using the performance weakness analysis engine, calculate the contribution of each competency dimension, identify competency weaknesses and strengths, and generate interpretable analysis results. Step S4: Based on skill gaps and employee skill profiles, the course intelligent matching engine retrieves candidate courses from the course knowledge graph, matches and sorts them, and generates personalized course recommendation results. Step S5: Track the employee's learning process through the dynamic learning path planning module, and dynamically adjust the learning path according to the learning effect and changes in ability; Step S6: Evaluate the performance changes before and after training through the training effectiveness evaluation and feedback module, and feed the evaluation results back to each module of the system to achieve closed-loop optimization; Step S7: Identify the characteristics of high-performing employees and construct a knowledge graph through the high-potential talent discovery and experience graph module to achieve experience reuse; Step S8: Perform causal analysis on the changes in ability and performance through the adaptive performance-ability causal evolution modeling module, and dynamically adjust the ability weights and course recommendation strategies based on the analysis results.

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