Student employment information management system based on artificial intelligence

Abstract

The invention discloses a student employment information management system based on artificial intelligence, and relates to the technical field of student employment management, and the system comprises the following components: an intelligent skill improvement planning module which constructs a skill map covering different industries and vocations, combines the vocational planning of students and the current skill level, and carries out the intelligent skill improvement planning; a personalized ability improvement plan is automatically generated by using a machine learning algorithm, students are helped to improve themselves in a targeted manner, transparency and normalization of a young employment market are significantly improved by introducing an honesty employment credit scoring system, and the system sets a negative list system for students with default behaviors, so that the system can improve the ability improvement efficiency of the students. According to the technical scheme, dishonest behaviors in the employment market are effectively restrained, the measure not only protects legal rights and interests of enterprises and avoids recruitment risks caused by information asymmetry, but also promotes honesty construction of student groups, improves credibility of the whole employment environment, and accurately evaluates and supervises credit of students.

Description

Technical Field

[0001] The present invention relates to the technical field of student employment management, and in particular to an artificial intelligence-based student employment information management system. Background Art

[0002] As the youth employment market continues to expand and become more complex, building a fair, just and efficient employment environment has become a common focus of attention from all walks of life. In this process, integrity, as the cornerstone of the market economy, plays an irreplaceable role in maintaining the order of the employment market and promoting the rational flow of young talents. However, traditional employment information management methods often focus on the collection and release of information, lacking an effective evaluation and supervision mechanism for the credit of both job seekers and companies, which may lead to dishonest behavior in the employment market.

[0003] Traditional employment market management mechanisms often rely on simple information review and moral constraints, and lack a scientific, systematic and comprehensive integrity evaluation system. Specifically, traditional methods are difficult to effectively identify and record the dishonest behavior of job seekers, which leads to companies facing the risk of information asymmetry during the recruitment process, increasing recruitment costs and difficulties. At the same time, traditional regulatory measures for corporate recruitment behavior are often prone to lag and are not detailed enough, making it difficult to effectively curb willful breach of contract, fraud and employment discrimination, and unable to provide job seekers with adequate rights protection. In addition, traditional technologies lack dynamic updates and real-time supervision mechanisms, making it difficult to adapt to the rapidly changing employment market environment.

[0004] Therefore, an artificial intelligence-based student employment information management system is developed to solve the above problems and provide students with more efficient, accurate and comprehensive employment information management and career planning services. Summary of the invention

[0005] The purpose of the present invention is to make up for the shortcomings of the existing technology and provide a student employment information management system based on artificial intelligence. It can accurately match students' skill needs with market dynamics by constructing skill maps and personalized ability improvement plans, significantly improve students' employment competitiveness, deeply analyze industry trends and corporate environment, and provide students with forward-looking career advice. The social interaction module enhances experience sharing and communication among users and improves students' interactivity. With efficient, accurate and comprehensive services, it helps students achieve their career planning goals and promotes the healthy development of the employment market.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a student employment information management system based on artificial intelligence, the system includes the following components:

[0007] Intelligent Skill Improvement Planning Module: Builds a skill map covering different industries and positions, combines students' career plans and current skill levels, and uses machine learning algorithms to automatically generate personalized ability improvement plans to help students improve themselves in a targeted manner and enhance their employment competitiveness;

[0008] Intelligent employment guidance and matching module: Introducing artificial intelligence technology to provide students with intelligent employment policy guidance, career planning, resume optimization, professional skill demand matching and job interview suggestions, and provide companies with accurate talent matching and employee growth planning services;

[0009] Social interaction module: Incorporate social elements to establish communication channels between college graduates, employers, employed groups and human resources service agencies. Students can use the channels to conduct basic social interaction and publish employment experiences and job-hunting skills.

[0010] Industry analysis and forecasting module: Introducing industry analysis reports and industry development prospect forecasting templates, combined with employee evaluations, to provide students with comprehensive corporate information, increase their understanding of target companies, and assist them in making more informed career choices;

[0011] Virtual reality experience module: Using virtual reality technology, students can be provided with a real experience of the working atmosphere and working mode of an enterprise, so as to help them understand the job content more intuitively and choose a job with a high sense of achievement and satisfaction;

[0012] Honest employment credit scoring system module: Establish an honest employment credit scoring system, set up a negative list system for students and companies with malicious behavior, guide companies and individuals to regulate their behavior, and ensure the healthy development of the youth employment market.

[0013] Furthermore, the intelligent skill improvement planning module collects data on skill requirements, knowledge frameworks and skill level standards from different industries, occupations and positions, organizes the collected data into a skill map using graph database technology, monitors market changes in real time, updates the skill map according to market changes, receives students' career goals, interests and expected time frames as career planning inputs, evaluates students' current skill levels through online questionnaires, analyzes the skill gaps between students and target positions based on students' career planning and skill assessment results, plans the optimal learning path in the skill map using the Dijkstra algorithm, recommends learning resources, practical projects and internship opportunities to students, and generates a personalized capability improvement plan that includes specific learning goals, time arrangements, learning resource links and practical project suggestions.

[0014] Furthermore, the intelligent skill improvement planning module uses graph database technology to organize the collected data into a skill graph, defines the nodes of the graph as skills, and the edges as the relationships between skills. A node is created for each skill in the graph database, and edges are created between corresponding nodes based on the collected information on the relationship between skills, and the weight w of the edge is calculated. ij , the weight calculation formula is: Where: w ij Indicates skills i and j The weight between k is the weight coefficient of the kth factor, and factor k (s i ,s j ) represent the skill s under the kth factor respectively. i and j The strength and importance of the association between them, n is the total number of factors, data sources are checked regularly, new skill nodes and edges are added, edge weights are adjusted according to market changes, and new relationships are added.

[0015] Furthermore, the intelligent skill improvement planning module uses the Dijkstra algorithm to plan the optimal learning path in the skill map, selects a source skill point as the starting point, initializes its distance value to 0, initializes the distance values ​​of all skill points to infinity, creates a set to save the skill points for which the shortest path has been found, constructs the adjacency matrix of the skill map to represent the connection relationship and weight between skills, selects a skill point closest to the source point from the unvisited skill points, adds it to the visited set, and for the currently selected skill point u, traverses all its adjacent skill points v, and calculates the skill points passing through the skill points. The distance d(u,v) from u to skill point v is calculated as follows: d(u,v)=d(u)+w(u,v), where d(u) is the known shortest distance from the source point to skill point u, w(u,v) is the weight of the edge from u to v, and the distance d(u,v) from skill point u to skill point v is less than the currently recorded distance d(v) of v. Update d(v) to d(u,v), and repeat the above steps until the target skill point is reached. Starting from the target skill point, trace back to the source point through the updated distance value to determine the shortest path, and output the shortest path and total distance from the source point to the target skill point.

[0016] Furthermore, the intelligent employment guidance and matching module collects student data and enterprise data from multiple sources, uses natural language processing technology to parse policy documents, extracts key information, provides personalized suggestions based on student data, classifies and recommends according to student interests, abilities and market demand, uses NLP and text generation technology to analyze resume content, makes optimization suggestions, uses cosine similarity to calculate the skill match between students and enterprise positions, uses historical interview data to train classification models, predicts interview success rates, and provides interview skills and suggestions. Based on the results of intelligent analysis, it generates employment policy guidance, career planning paths, resume optimization suggestions and job interview strategies for students, builds a talent matching model, and makes precise matches based on enterprise job requirements and student abilities.

[0017] Furthermore, the intelligent employment guidance and matching module uses cosine similarity to calculate the skill matching between students and enterprise positions, and constructs a student skill vector S = [s1, s2, s3, ..., s m ] and enterprise job skill vector J = [j1,j2,j3,…,j n ], where m and n represent the total number of students and the total number of enterprise positions, respectively. Cosine similarity is used as the similarity metric to calculate the similarity V between students and enterprise positions. Sj , cosine similarity calculation formula: Where: s m and j n are the skill vectors of student S and position J, s m ·j n represents the dot product of the student skill vector and the enterprise job skill vector, |s m | and |j n |represent the modulus of the student skill vector and the enterprise position skill vector respectively. For each student, all positions are sorted according to the predicted matching degree, and the position with the highest matching degree is recommended.

[0018] Furthermore, the industry analysis and forecasting module collects industry research reports issued by investment banks, securities firms, human resource intermediaries and management consulting firms, collects corporate employees' evaluations of the industry, company culture and work environment, obtains information on market size, growth rate and competitive landscape from government departments, industry associations and corporate annual reports, cleans and standardizes data, deeply interprets industry analysis reports, uses the ARIMA model in combination with historical data to predict the future development direction of the industry, conducts statistical analysis on employee evaluation data, identifies employee satisfaction and concerns about various aspects of the company, integrates industry analysis reports, development prospect forecast results and employee evaluation analysis to form a comprehensive corporate information report.

[0019] Furthermore, the industry analysis and forecasting module uses the ARIMA model in combination with historical data to predict the future development direction of the industry, divides the preprocessed historical data into a training set and a test set, visualizes the data, draws a time series graph, observes the trend, seasonality and cyclical characteristics of the data, performs an ADF test on the data to verify the stability of the data, and when the data is non-stationary, performs differential processing until the data becomes stable, calculates ACF and PACF to identify the ARIMA model parameters, p is the number of autoregressive terms, d is the number of differences, q is the number of moving average terms, and establishes the ARIMA model based on the determined parameters. The ARIMA model formula is: Where: B is the backoff operator, φ i is the autoregressive coefficient, θ j is the moving average coefficient, ∈ t It is a white noise error term. The maximum likelihood estimation method is used to estimate the model parameters to check whether the residual sequence is white noise, to ensure the fit of the model, the training set is used to fit the model, the test set is used to evaluate the prediction performance of the model, and the fitted ARIMA model is used to predict future values.

[0020] Furthermore, the virtual reality experience module collects the expectations and needs of students, enterprises and industry experts for the VR experience module through questionnaires, uses professional 3D modeling software to create high-precision models of enterprise working environments, equipment and characters, and imports them into the VR development platform, designs intuitive and easy-to-use interactive methods, so that students can freely explore and interact with virtual objects in the virtual environment, designs specific work content simulations and task challenges for different positions, provides personalized experience paths and task recommendations based on students' personal interests, ability levels and learning progress, conducts strict performance optimization and compatibility testing on the VR experience module, deploys the optimized VR experience module on the designated VR hardware platform, collects students' feedback on the use of the VR experience module, and evaluates its satisfaction and effectiveness.

[0021] Furthermore, the process of determining the coverage of the credit scoring system by the honest employment credit scoring system module is as follows:

[0022] Extensively collect questionnaire data from enterprises and students, and collect enterprise employees' evaluations of the industry, company culture, salary, career growth potential and working environment. Since the evaluation data includes structured data and unstructured data, unstructured data has no fixed format and is more diverse and complex, but it usually reflects the real needs, interests and utility levels of enterprises and students. Relevant unstructured texts are randomly extracted at the same time as texts containing keywords to form a library of sentences to be marked, and the library of marked sentences is manually annotated to form training sets, test sets and verification sets to determine whether the individuals under investigation have credit risks;

[0023] Preprocess and analyze data through natural language processing and machine learning technologies, integrate all feature indicators into a unified database, identify key indicators that affect credit scores, and use supervised machine learning methods such as the large language model ERNIE to train sentence classification models and construct credit risk indicators;

[0024] The key credit scoring indicators are weighted by a combination of subjective and objective weighting methods. In terms of the subjective weighting method, the expert scoring method is combined with the artificial intelligence software scoring method. In terms of the artificial intelligence software scoring method, artificial intelligence plays the role of a "big language model employer-employee credit risk assessor" to score the weights of each content in the questionnaire and obtain the artificial intelligence software scoring weight. In terms of the objective weighting method, the entropy weight method, CRITIC method and TOPSISI method are combined to calculate the comprehensive weight;

[0025] According to the selected key indicators and weights, the logistic regression model in the machine learning algorithm is used to develop a credit scoring model suitable for this system. The mathematical expression of the logistic regression model is: Where: P(Y=1|X) is the probability of default given feature X, X1,…,X n are the characteristic variables, β0,…,β n It is a model parameter. The model is used to predict the credit status of enterprises and individuals. The standards for malicious behavior are defined and the process for entering, updating and removing negative lists is designed. The credit scoring model is applied to enterprises and individuals, credit scores are generated, credit scores are updated regularly, and credit scoring results are presented in a tabular form for employers and job seekers. Based on the credit scoring results, suggestions are provided to employers for screening job seekers and suggestions are provided to job seekers for improving their credit status.

[0026] Compared with the existing technology, this student employment information management system based on artificial intelligence has the following advantages:

[0027] Beneficial effects:

[0028] 2. The present invention also sets up a negative list system for enterprises that arbitrarily breach contracts, are suspected of fraud, or engage in employment discrimination, further regulating the recruitment behavior of enterprises. This innovative measure not only protects the legitimate rights and interests of job-seeking students and prevents them from unfair treatment and discrimination, but also encourages enterprises to strengthen internal management and improve the standardization and transparency of the recruitment process. Through the warning effect of the negative list, enterprises will treat recruitment activities more cautiously, reduce malicious competition and bad behavior, thereby creating a more harmonious and orderly employment market environment. In the long run, this will help improve the overall efficiency and quality of the youth employment market and promote the sustained and healthy development of the economy and society.

[0029] 2. The present invention also sets up a negative list system for enterprises that arbitrarily breach contracts, are suspected of fraud, or engage in employment discrimination, further regulating the recruitment behavior of enterprises. This innovative measure not only protects the legitimate rights and interests of job-seeking students and prevents them from unfair treatment and discrimination, but also encourages enterprises to strengthen internal management and improve the standardization and transparency of the recruitment process. Through the warning effect of the negative list, enterprises will treat recruitment activities more cautiously, reduce malicious competition and bad behavior, thereby creating a more harmonious and orderly employment market environment. In the long run, this will help improve the overall efficiency and quality of the youth employment market and promote the sustained and healthy development of the economy and society.

[0030] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0032] Figure 1 This is a flow chart of a student employment information management system based on artificial intelligence. DETAILED DESCRIPTION

[0033] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0034] Embodiment 1:

[0035] This embodiment provides a student employment information management system based on artificial intelligence. The system achieves accurate guidance and efficient support for students' career planning by constructing a dynamically updated skill map and personalized ability improvement plan. It can not only track market trends in real time to ensure that students' skills are synchronized with industry needs, but also provide students with forward-looking career choice suggestions through in-depth industry insights and corporate evaluation analysis. The social interaction module builds a bridge for students to exchange experiences and share insights, enhances user stickiness, and promotes the dissemination and sharing of knowledge.

[0036] The intelligent skill improvement planning module collects data on skill requirements, knowledge frameworks and skill level standards from different industries, occupations and positions, organizes the collected data into a skill map using graph database technology, monitors market changes in real time, and updates the skill map accordingly. It receives students' career goals, interests and expected time frames as career planning inputs, evaluates students' current skill levels through online questionnaires, and uses machine learning algorithms to conduct demand analysis based on students' career planning and skill assessment results to identify the skill gap between students and target positions. It uses the Dijkstra algorithm to plan the optimal learning path from the current skill level to the skills required for the target position in the skill map, combines the recommendation system algorithm to recommend the most suitable learning resources for students, uses similarity calculation to recommend suitable practice projects and internship opportunities based on the matching degree between practice project requirements and student capabilities, and generates a personalized capability improvement plan that includes specific learning goals, time arrangements, learning resource links and practice project suggestions.

[0037] The intelligent employment guidance and matching module collects student data and enterprise data from school management systems, social media, corporate recruitment websites, and policy release platforms, removes duplicate data, corrects erroneous data, fills in missing values, converts all data into a unified format and dimension, parses government and school employment policy documents, extracts key information, and provides targeted policy interpretations and suggestions based on student personal data. Based on student interests, abilities, and market demand, it uses machine learning algorithms to build a career classification model. Based on the classification results, it recommends suitable career directions and corresponding positions to students, analyzes the content of resumes submitted by students, identifies potential problems, and makes optimization suggestions. Based on the optimization suggestions, it automatically generates and assists students in modifying resumes, builds a scoring matrix for students (users) and positions (projects), and uses cosine similarity to calculate the similarity between students and positions. The cosine similarity calculation formula is: Based on user-user collaborative filtering: The formula is: For unevaluated positions, predictions are made based on the evaluations of similar student groups and based on project-project collaborative filtering. For new students, predictions are made based on the evaluations of similar positions. Accurate matches are made based on corporate job requirements and student capabilities, providing corporate employees with personalized growth planning suggestions based on their career paths and personal development.

[0038] The social interaction module is designed with an intuitive and easy-to-use user interface to ensure that different user groups can easily access and quickly get started. It supports registration of multiple types of users and provides an identity authentication mechanism. Exclusive labels and badges are set for different user groups to facilitate distinction and identification. Multiple theme sections are created to meet the needs of different users. Based on the user's browsing history, topics of interest, and published content data, relevant posts, users, and groups are recommended to users. The user's behavioral data on the platform is collected and analyzed to provide a basis for platform optimization and strategy adjustment.

[0039] The industry analysis and forecasting module regularly obtains the latest industry research reports from investment banks, securities companies, and management consulting companies, collects employees' evaluations of the industry, company culture, and work environment, obtains key data on market size, growth rate, and competitive landscape from government departments, industry associations, and corporate annual reports, cleans the collected data, removes duplicate and erroneous data, and unifies the format and dimension to ensure data quality. It uses natural language processing technology (NLP) to parse industry research reports, extract key information, and conducts in-depth interpretation of the extracted information. It uses the ARIMA model to combine historical data and current trends, divides historical data into training sets and test sets, and visualizes historical data. Analyze the trend, seasonality, and cyclical characteristics of the data, perform ADF tests on the data to test the stability of the data. When the data is non-stationary, perform differential processing until the data becomes stable, calculate ACF and PACF to identify the ARIMA model parameters (p, d, q), where p is the number of autoregressive terms, d is the number of differentials, and q is the number of moving average terms. According to the determined parameters, establish the ARIMA (p, d, q) model. The ARIMA model formula is: The maximum likelihood estimation method is used to estimate the model parameters, check whether the residual sequence is white noise, ensure the model fit, use the training set to fit the model, and the test set to evaluate the model performance. The fitted model is used to predict future values, and statistical analysis is performed on employee evaluation data to identify employees' satisfaction and concerns about various aspects of the company. Combined with the industry trend forecast results, the company's competitiveness and potential risks in the industry are analyzed. The industry analysis report, development prospect forecast results and employee evaluation analysis are integrated to form a comprehensive corporate information report, providing students with targeted career choice suggestions, including industry selection, job recommendation, and career planning path. Students' feedback on career suggestions is collected, the effectiveness and satisfaction of the suggestions are evaluated, and the model algorithm and data collection methods are continuously optimized based on the feedback results to improve the accuracy and personalization of career suggestions.

[0040] For the virtual reality experience module, we designed and distributed questionnaires for students, enterprises and industry experts to collect their expectations and needs for the functions, content and interaction methods of the VR experience module. We used professional 3D modeling software to create high-precision models of the enterprise's working environment, equipment and characters, and imported them into the VR development platform. We imported the created 3D models into the VR development platform to build scenes and layouts, and designed intuitive and easy-to-use interaction methods, such as gesture recognition and voice control, so that students can freely explore and interact with virtual objects in the virtual environment. We designed specific work content simulations and task challenges for different positions. We intelligently recommended suitable experience paths and task challenges based on students' personal interests, ability levels and learning progress to improve the pertinence and effectiveness of learning. We strictly optimized the performance of the VR experience module, including reducing rendering load, optimizing algorithm complexity, and improving resource utilization to ensure the smooth operation of the module on the VR hardware platform. We deployed the optimized VR experience module to the designated VR hardware platform and performed final debugging and testing. We collected students' feedback on the use of the VR experience module and evaluated its satisfaction and effectiveness. Based on the feedback results, we continuously iterated and optimized the content, functions and interaction methods of the VR experience module to meet the changing needs of students and improve user experience.

[0041] The module of the credit scoring system for honest employment designs and widely distributes questionnaires to enterprises and students. The questionnaire content covers multiple dimensions such as industry evaluation, company culture, work environment, and personal credit behavior. It collects evaluations and feedback from employees on industry trends, company atmosphere, and specific work content to ensure that the data is comprehensive and representative. The collected unstructured text data is randomly extracted and keyword-screened to form a library of sentences to be marked. A professional team is organized to conduct detailed manual annotation of the library of marked sentences to distinguish the content related to credit risk to form training sets, test sets, and validation sets. Natural language processing technology (NLP) is used to clean, segment, and remove stop words from the data to improve data quality. Machine learning technology is used to identify and extract key information from the text to lay the foundation for subsequent analysis. All processed feature indicators are integrated into a unified database. Large language models and machine learning algorithms are used to identify key indicators that affect credit scores. Based on supervised machine learning methods, large language models such as ERNIE are used to train sentence classification models to construct effective credit risk indicators. The accuracy of the model's prediction of credit risk is improved by continuously adjusting model parameters and optimizing algorithms. Combining the expert scoring method with the artificial intelligence software scoring method, the artificial intelligence software scoring method allows artificial intelligence to play the role of a "big language model employer-employee credit risk assessor" to score the weights of each content in the questionnaire and obtain the artificial intelligence software scoring weights. In terms of the objective weighting method, the entropy weight method, CRITIC method and TOPSISI method are combined to calculate the comprehensive weights, and the logistic regression model is used to develop a credit scoring model suitable for this system. The model expression is: Where: P(Y=1|X) is the probability of default given feature X, X1,…,X n are the characteristic variables, β0,…,β n It is a model parameter. The relevant information of enterprises and individuals is input into the credit scoring model to generate corresponding credit scores. The credit scoring data is updated regularly. According to the credit scoring results, malicious behavior standards are defined, and the entry, update and removal processes of the negative list are designed. Negative list management is implemented for enterprises and individuals with dishonest behavior to restrict their activities in the employment market. The credit scoring results are displayed to recruiters and job seekers in the form of charts to provide an intuitive credit status reference.

[0042] Embodiment 2:

[0043] This embodiment particularly emphasizes the application of the intelligent skill improvement planning module in an artificial intelligence-based student employment information management system:

[0044] We collected data on skill requirements, knowledge frameworks, and skill level standards from industry reports, job descriptions, online course platforms, and skill certification agencies. We cleaned the collected data to remove duplicate, erroneous, and irrelevant information to ensure the accuracy and validity of the data. We used natural language processing technology to vectorize the skill descriptions and extract the association information between skills. We defined the nodes of the graph as skills and the edges as the relationships between skills. We created a node for each skill in the graph database and created edges between the corresponding nodes based on the association information between skills. We calculated the weights of the edges to represent the strength and importance of the relationships between skills. The weight calculation formula is: Regularly check data sources, add new skill nodes and edges, and adjust edge weights and add new relationships based on market changes.

[0045] Receive students' career goals, interests and expected time frames as career planning inputs, assess students' current skill levels through online questionnaires, and map the assessment results to a skill map. Use machine learning algorithms to analyze students' career plans and skill assessment results to identify the skill gaps between students and target positions. Use machine learning algorithms to analyze students' career plans and skill assessment results to identify the skill gaps between students and target positions.

[0046] Use the Dijkstra algorithm to plan the optimal learning path in the skill graph, initialize the distance array dist, which represents the shortest path length from the starting point to each node, create a priority queue Q, which initially contains the starting point node, and loop to take out the node with the smallest dist value from the priority queue. The alt calculation formula is: alt = dist[u] + w(u,v), and update the dist value of its adjacent node. The formula is: dist[v] = alt. Repeat the above steps until the priority queue is empty, backtrack from the target node to the starting point, build the shortest path, and output the shortest path skill node sequence obtained by backtracking as the learning path.

[0047] Combined with the recommendation system algorithm, the most suitable learning resources are recommended based on students' interests, learning history and popular learning resources in the market. Similarity calculation is used to recommend practical projects and internship opportunities that match students' abilities and interests.

[0048] Based on the above steps, a personalized capacity improvement plan is generated that includes specific learning goals, time arrangements, learning resource links and practical project suggestions. The capacity improvement plan is dynamically adjusted according to students' learning progress, evaluation results and feedback to ensure the plan's continued effectiveness and pertinence.

[0049] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A student employment information management system based on artificial intelligence, characterized in that: The system includes: Intelligent Skill Improvement Planning Module: Builds a skill map covering different industries and positions, combines students' career plans and current skill levels, and uses machine learning algorithms to automatically generate personalized ability improvement plans to help students improve themselves in a targeted manner and enhance their employment competitiveness; Intelligent employment guidance and matching module: Introducing artificial intelligence technology to provide students with intelligent employment policy guidance, career planning, resume optimization, professional skill demand matching and job interview suggestions, and provide companies with accurate talent matching and employee growth planning services; Social interaction module: Incorporate social elements to establish communication channels between college graduates, employers, employed groups and human resources service agencies. Students can use the channels to conduct basic social interaction and publish employment experiences and job-hunting skills. Industry analysis and forecasting module: Introducing industry analysis reports and industry development prospect forecasting templates, combined with employee evaluations, to provide students with comprehensive corporate information, increase their understanding of target companies, and assist them in making more informed career choices; Virtual reality experience module: Using virtual reality technology, students can be provided with a real experience of the working atmosphere and working mode of an enterprise, so as to help them understand the job content more intuitively and choose a job with a high sense of achievement and satisfaction; Honest employment credit scoring system module: Establish an honest employment credit scoring system, set up a negative list system for students and companies with malicious behavior, guide companies and individuals to regulate their behavior, and ensure the healthy development of the youth employment market.

2. According to claim 1, a student employment information management system based on artificial intelligence is characterized in that: The intelligent skill improvement planning module collects data on skill requirements, knowledge frameworks and skill level standards from different industries, occupations and positions, organizes the collected data into a skill map using graph database technology, monitors market changes in real time, updates the skill map according to market changes, receives students' career goals, interests and expected time frames as career planning inputs, evaluates students' current skill levels through online questionnaires, analyzes the skill gaps between students and target positions based on students' career planning and skill assessment results, plans the optimal learning path in the skill map using the Dijkstra algorithm, recommends learning resources, practical projects and internship opportunities to students, and generates a personalized capability improvement plan that includes specific learning goals, time arrangements, learning resource links and practical project suggestions.

3. According to claim 2, a student employment information management system based on artificial intelligence is characterized in that: The intelligent skill improvement planning module uses graph database technology to organize the collected data into a skill graph, defines the nodes of the graph as skills, and the edges as the relationship between skills. A node is created for each skill in the graph database, and edges are created between corresponding nodes based on the collected information on the relationship between skills, and the weight w of the edge is calculated. ij , the weight calculation formula is: Where: w ij Indicates skills i and j The weight between k is the weight coefficient of the kth factor, and factor k (s i ,s j Respectively represent the skill s under the kth factor i and j The strength and importance of the association between them, n is the total number of factors, data sources are checked regularly, new skill nodes and edges are added, edge weights are adjusted according to market changes, and new relationships are added.

4. According to claim 2, a student employment information management system based on artificial intelligence is characterized in that: The intelligent skill improvement planning module plans the optimal learning path in the skill map using the Dijkstra algorithm, selects a source skill point as the starting point, initializes its distance value to 0, initializes the distance values ​​of all skill points to infinity, creates a set to save the skill points for which the shortest path has been found, constructs an adjacency matrix of the skill map to represent the connection relationship and weight between skills, selects a skill point closest to the source point from the unvisited skill points, adds it to the visited set, and for the currently selected skill point u, traverses all its adjacent skill points v, and calculates the distance from the skill point u to the skill point v. The distance d(u,v) to skill point v is calculated as follows: d(u,v)=d(u)+w(u,v), where d(u) is the known shortest distance from the source point to skill point u, w(u,v) is the weight of the edge from u to v, and the distance d(u,v) from skill point u to skill point v is less than the currently recorded distance d(v) of v. Update d(v) to d(u,v), and repeat the above steps until the target skill point is reached. Starting from the target skill point, trace back to the source point through the updated distance value to determine the shortest path, and output the shortest path and total distance from the source point to the target skill point.

5. According to claim 1, a student employment information management system based on artificial intelligence is characterized in that: The intelligent employment guidance and matching module collects student data and enterprise data from multiple sources, uses natural language processing technology to parse policy documents, extracts key information, provides personalized suggestions based on student data, classifies and recommends based on student interests, abilities and market demand, uses NLP and text generation technology to analyze resume content, makes optimization suggestions, uses cosine similarity to calculate the skill match between students and enterprise positions, uses historical interview data to train classification models, predicts interview success rates, and provides interview skills and suggestions. Based on the results of intelligent analysis, it generates employment policy guidance, career planning paths, resume optimization suggestions and job interview strategies for students, builds a talent matching model, and makes precise matches based on enterprise job requirements and student abilities.

6. According to claim 5, a student employment information management system based on artificial intelligence is characterized in that: The intelligent employment guidance and matching module uses cosine similarity to calculate the skill matching between students and enterprise positions, and constructs a student skill vector S = [s1, s2, s3, ..., s m ] and enterprise job skill vector J = j1, j2, j3, …, j n ], where m and n represent the total number of students and the total number of enterprise positions, respectively. Cosine similarity is used as the similarity metric to calculate the similarity V between students and enterprise positions. Sj , cosine similarity calculation formula: Where: s m and j n are the skill vectors of student S and position J, s m ·j n represents the dot product of the student skill vector and the enterprise job skill vector, |s m | and |j n |represent the modulus of the student skill vector and the enterprise position skill vector respectively. For each student, all positions are sorted according to the predicted matching degree, and the position with the highest matching degree is recommended.

7. According to claim 1, a student employment information management system based on artificial intelligence is characterized in that: The industry analysis and forecasting module collects industry research reports issued by investment banks, securities firms, human resource intermediaries and management consulting firms, collects corporate employees' evaluations of the industry, corporate culture and work environment, obtains information on market size, growth rate and competitive landscape from government departments, industry associations and corporate annual reports, cleans and standardizes data, deeply interprets industry analysis reports, uses the ARIMA model in combination with historical data to predict the future development direction of the industry, conducts statistical analysis on employee evaluation data, identifies employee satisfaction and concerns about various aspects of the company, integrates industry analysis reports, development prospect forecast results and employee evaluation analysis to form a comprehensive corporate information report.

8. According to claim 7, a student employment information management system based on artificial intelligence is characterized in that: The industry analysis and forecasting module uses the ARIMA model combined with historical data to predict the future development direction of the industry, divides the preprocessed historical data into a training set and a test set, visualizes the data, draws a time series graph, observes the trend, seasonality and periodicity of the data, and performs an ADF test on the data to verify the stability of the data. When the data is non-stationary, perform differential processing until the data becomes stable, calculate ACF and PACF to identify the ARIMA model parameters, p is the number of autoregressive terms, d is the number of differences, q is the number of moving average terms, and establishes the ARIMA model based on the determined parameters. The ARIMA model formula is: Where: B is the backoff operator, φ i is the autoregressive coefficient, θ j is the moving average coefficient, ∈ t It is a white noise error term. The maximum likelihood estimation method is used to estimate the model parameters to check whether the residual sequence is white noise, to ensure the fit of the model, the training set is used to fit the model, the test set is used to evaluate the prediction performance of the model, and the fitted ARIMA model is used to predict future values.

9. According to claim 1, a student employment information management system based on artificial intelligence is characterized in that: The virtual reality experience module collects the expectations and needs of students, enterprises and industry experts for the VR experience module through questionnaires, uses professional 3D modeling software to create high-precision models of enterprise working environments, equipment and characters, and imports them into VR development platforms, designs intuitive and easy-to-use interactive methods, so that students can freely explore and interact with virtual objects in virtual environments, designs specific work content simulations and task challenges for different positions, provides personalized experience paths and task recommendations based on students' personal interests, ability levels and learning progress, conducts strict performance optimization and compatibility testing on the VR experience module, deploys the optimized VR experience module on the designated VR hardware platform, collects students' feedback on the use of the VR experience module, and evaluates its satisfaction and effectiveness.

10. The student employment information management system based on artificial intelligence according to claim 1, characterized in that: The process of determining the coverage of the credit scoring system by the honest employment credit scoring system module is as follows: Extensively collect questionnaire data from enterprises and students, and collect enterprise employees' evaluations of the industry, company culture, salary, career growth potential and working environment. Since the evaluation data includes structured data and unstructured data, unstructured data has no fixed format and is more diverse and complex, but it usually reflects the real needs, interests and utility levels of enterprises and students. Relevant unstructured texts are randomly extracted at the same time as texts containing keywords to form a library of sentences to be marked, and the library of marked sentences is manually annotated to form training sets, test sets and verification sets to determine whether the individuals under investigation have credit risks; Preprocess and analyze data through natural language processing and machine learning technologies, integrate all feature indicators into a unified database, identify key indicators that affect credit scores, and use supervised machine learning methods such as the large language model ERNIE to train sentence classification models and construct credit risk indicators; The key credit scoring indicators are weighted by a combination of subjective and objective weighting methods. In terms of the subjective weighting method, the expert scoring method is combined with the artificial intelligence software scoring method. In terms of the artificial intelligence software scoring method, artificial intelligence plays the role of a "big language model employer-employee credit risk assessor" to score the weights of each content in the questionnaire and obtain the artificial intelligence software scoring weight. In terms of the objective weighting method, the entropy weight method, CRITIC method and TOPSISI method are combined to calculate the comprehensive weight; According to the selected key indicators and weights, the logistic regression model in the machine learning algorithm is used to develop a credit scoring model suitable for this system. The mathematical expression of the logistic regression model is: Where: P(Y=1|X) is the probability of default given feature X, X1,…,X n are the characteristic variables, β0,…,β n It is a model parameter. The model is used to predict the credit status of enterprises and individuals. The standards for malicious behavior are defined and the process for entering, updating and removing negative lists is designed. The credit scoring model is applied to enterprises and individuals, credit scores are generated, credit scores are updated regularly, and credit scoring results are displayed in a form of tables for employers and job seekers. Based on the credit scoring results, suggestions are provided to employers for screening job seekers and suggestions are provided to job seekers for improving their credit status.

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