Human resource management method and system based on artificial intelligence
By using AI-based methods to conduct intelligent Q&A assessments and job matching for candidates, combined with training recommendations and turnover prediction, this approach addresses the inefficiencies and subjective biases inherent in traditional human resource management, achieving more efficient and equitable recruitment and employee management.
Patent Information
- Application Number
- CN202511095759.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-06
- Publication Date
- 2025-11-21
AI Technical Summary
Traditional human resource management systems require manual processing and reading of a large number of resumes, which is prone to omissions or errors and is influenced by subjective biases, resulting in low recruitment efficiency and limited development opportunities for outstanding talents.
Using an AI-based approach, the system analyzes the logic and keyword matching of candidates' answers through speech recognition, computer vision, and NLP. It also assesses candidates' emotional stability and communication skills, automatically matches them with job requirements, recommends personalized training courses, and predicts turnover probability through logistic regression, generating optimization suggestions.
It enables objective assessment of candidates' abilities, improves recruitment accuracy and fairness, increases recruitment efficiency, reduces the influence of subjective judgment, promptly identifies employee problems and takes intervention measures, and promotes employee career development.
Smart Images
Figure CN120996767A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of human resource information management technology, and in particular to a human resource management method and system based on artificial intelligence. Background Technology
[0002] A human resource management system is a system that uses information technology to support and manage a company's human resource activities. It aims to help companies recruit, train, manage, and develop employees more effectively. Human resource management includes forecasting human resource needs and making human resource demand plans, recruiting personnel, and conducting effective performance evaluations to meet the company's current and future development needs.
[0003] However, traditional human resource management systems typically require manual processing and reading of large amounts of resumes and candidate information. This involves reading, understanding, and comparing texts, which can easily lead to omissions or errors and reduce recruitment efficiency. Furthermore, during the human resource management process, human resource specialists may be influenced by subjective biases, limiting development opportunities for outstanding talent.
[0004] Therefore, it is necessary to provide a new artificial intelligence-based human resource management method and system to solve the above-mentioned technical problems. Summary of the Invention
[0005] To address the aforementioned technical problems, this invention provides a human resource management method and system based on artificial intelligence.
[0006] The artificial intelligence-based human resource management method provided by this invention includes the following steps:
[0007] S1. In the recruitment screening stage, we collect and screen the resumes of applicants, conduct intelligent Q&A evaluation of the screened resumes, analyze the matching degree between the resumes and the job openings, and select to publish a draft employment contract based on the results of the intelligent Q&A evaluation and the matching degree.
[0008] S2. During the onboarding management phase, the performance, attendance, satisfaction, project completion rate, and departmental revenue of new employees are recorded, and collaborative filtering algorithms are used to recommend training courses.
[0009] S3. In the risk prediction stage, logistic regression is used to predict the turnover probability based on the performance, attendance and satisfaction of new employees. Combined with departmental revenue and project completion rate, per capita output is calculated and optimization suggestions are generated.
[0010] Furthermore, the candidate intelligent question-and-answer evaluation includes: analyzing the logicality and keyword matching degree of the candidate's answers through speech recognition and NLP, analyzing the candidate's emotional stability and communication ability by capturing facial expressions and body language through computer vision, and generating a question-and-answer score based on logicality, keyword matching degree, emotional stability, and communication ability.
[0011] Furthermore, recommending training courses using collaborative filtering algorithms includes the following steps:
[0012] Step 1: Data Collection: Collect data on performance, skills, career goals, and courses;
[0013] Step 2: Feature construction, including performance features, skill features, career goal codes, skill tag vectors, and goal matching degree;
[0014] Step 3: Construct a rating matrix to determine explicit and implicit ratings;
[0015] Step 4: Course Recommendation.
[0016] Furthermore, the per capita output is calculated as: department revenue / number of employees * project completion rate.
[0017] The present invention provides a system for implementing an artificial intelligence-based human resource management method, comprising: an intelligent recruitment module for receiving and screening resume information, and performing intelligent question-and-answer evaluation on the screened resume information;
[0018] The personnel management module is used to arrange the onboarding and training process based on the candidate evaluation results, and to record the performance, attendance, satisfaction, project completion rate and department revenue of the onboarded personnel.
[0019] The data analysis module is used to predict the turnover probability based on the performance, attendance and satisfaction of new employees using logistic regression, and to calculate the output per person and generate optimization suggestions by combining department revenue and project completion rate.
[0020] Furthermore, the intelligent recruitment module includes: a job posting unit, used to create job posting information;
[0021] The data collection unit is used to collect and filter job application resume information and industry-related knowledge semantics.
[0022] The intelligent consultation testing unit is used to build a question-and-answer knowledge base based on industry-related semantic knowledge, and to generate question-and-answer scores by recognizing the logic of candidates' answers and the degree of keyword matching.
[0023] The job matching unit is used to identify and extract key information from applicant resumes and calculate the matching degree between the key information in the resume and the job information by using cosine similarity.
[0024] The Employment Contract Draft Unit is used to select and publish employment contract drafts based on the candidates' Q&A scores and the matching degree between key information in their resumes and the job postings.
[0025] The approval management unit is used to review and process draft employment contracts.
[0026] Furthermore, the personnel management module includes:
[0027] Personalized training modules recommend training courses based on employee performance, skills assessment, and career goals using collaborative filtering algorithms;
[0028] The intelligent performance management unit is used to track the performance, attendance, satisfaction, project completion rate and department revenue of new employees in real time. It combines self-evaluation, superior rating and peer evaluation to generate a comprehensive score through a weighted algorithm.
[0029] The employee intelligent question-and-answer unit answers employee questions based on a question-and-answer knowledge base and NLP analysis.
[0030] Furthermore, the data analysis module includes: a predictive analysis unit, which uses logistic regression to predict employee performance trends based on the performance, attendance, and satisfaction of new employees, provides early warnings of potential decline risks and turnover probabilities, and calculates per capita output and generates optimization suggestions by combining departmental revenue and project completion rate;
[0031] The data visualization unit displays key indicators such as recruitment information, employee turnover rate, and training effectiveness in real time.
[0032] Furthermore, the process of identifying and extracting key information from job application resumes includes: using NLP to automatically parse resumes in document format and extracting key information from the document information.
[0033] Furthermore, the key information includes the candidate's skills, project experience, and educational background.
[0034] Compared with related technologies, the artificial intelligence-based human resource management method and system provided by this invention have the following beneficial effects:
[0035] 1. This invention establishes a question-and-answer database based on industry-related knowledge semantics, ensuring that the assessment content is highly relevant to job requirements. Through speech recognition and NLP analysis of logicality and keyword matching, combined with computer vision analysis of emotional stability and communication skills, it achieves a comprehensive assessment of candidates' abilities. Based on objective data scoring, it avoids subjective human judgment, improving recruitment fairness. By analyzing the similarity between key information in resumes (skills, project experience, educational background) and job requirements, it accurately screens candidates with high matching degrees, improving recruitment quality. Based on scores and matching degrees, it automatically generates contract drafts, reducing manual intervention and improving recruitment process efficiency.
[0036] 2. Based on employee performance, skills assessment and career goals, this invention recommends personalized training courses to improve the matching degree between employee skills and positions, promote career development, reduce skills gap, monitor performance, attendance and satisfaction data in real time, promptly identify employee problems (such as declining satisfaction) and take early intervention measures. Based on performance, attendance and satisfaction data, it can identify high-risk employees and the probability of turnover in advance. Attached Figure Description
[0037] Figure 1 The structural block diagram of the artificial intelligence-based human resource management system provided by the present invention;
[0038] Figure 2 A flowchart illustrating the artificial intelligence-based human resource management method provided by this invention;
[0039] Figure 3 The flowchart provided by this invention describes the recommendation of training courses using a collaborative filtering algorithm. Detailed Implementation
[0040] The present invention will be further described below with reference to the accompanying drawings and embodiments.
[0041] Please refer to the following: Figure 1 , Figure 2 , Figure 3 ,in, Figure 1 The structural block diagram of the artificial intelligence-based human resource management system provided by the present invention; Figure 2 A flowchart illustrating the artificial intelligence-based human resource management method provided by this invention; Figure 3 The flowchart provided by this invention describes the recommendation of training courses using a collaborative filtering algorithm.
[0042] Example 1
[0043] In the specific implementation process, such as Figure 2 As shown, an artificial intelligence-based human resource management method includes the following steps:
[0044] S1. In the recruitment screening stage, we collect and screen the resumes of applicants, conduct intelligent Q&A evaluation of the screened resumes, analyze the matching degree between the resumes and the job openings, and select to publish a draft employment contract based on the results of the intelligent Q&A evaluation and the matching degree.
[0045] S2. During the onboarding management phase, the performance, attendance, satisfaction, project completion rate, and departmental revenue of new employees are recorded, and collaborative filtering algorithms are used to recommend training courses.
[0046] S3. In the risk prediction stage, logistic regression is used to predict the turnover probability based on the performance, attendance and satisfaction of new employees. The output per employee is calculated by combining department revenue and project completion rate and optimization suggestions are generated. The output per employee is calculated as: department revenue / number of employees * project completion rate.
[0047] For example: Part A: Revenue of 5 million yuan, number of employees of 10, project completion rate of 85%;
[0048]
[0049] Using logistic regression to predict the probability of job turnover includes the following:
[0050] In this embodiment of the invention, in order to predict the probability of employee turnover, it is necessary to make predictions based on the performance, attendance, and satisfaction of the new employees. The formula for predicting the probability of turnover is as follows:
[0051] Data example:
[0052] Suppose an employee dataset contains the following characteristics:
[0053]
[0054]
[0055] The above data is divided into a training set (80%) and a test set (20%), with the first 8 data points being the training set and the last 2 data points being the test set;
[0056] Data standardization: Where μ is the mean of the features in the training set, and σ is the standard deviation of the features in the training set;
[0057] Example calculation (taking "monthly absent days" as an example)
[0058] Training set data: [2, 5, 0, 8, 1, 6, 0, 10]
[0059] mean
[0060] Standard deviation
[0061] Standardized test set data (employee IDs: 9 and 10)
[0062] Employee 9:
[0063] Employee 10:
[0064] The logistic regression model training and prediction are as follows:
[0065] The model formula includes linear combinations and the Sigmoid function;
[0066] Linear combination: h(x) = w0 + w1x1 + w2x2 + w3x3 + w4x4 + w5x5
[0067] Where x1 is the standardized performance score, x2 is the standardized monthly absence days, x3 is the standardized satisfaction score, x4 is the standardized department revenue, and x5 is the standardized project completion rate.
[0068] Sigmoid function:
[0069] The output is the probability of leaving the company (between 0 and 1);
[0070] The loss function is as follows:
[0071] Where m is the number of training samples, y i For the actual label (0 or 1), To predict probabilities;
[0072] Here is an example of predicting employee ID9:
[0073] The trained model parameters are:
[0074] Intercept w0 = 0.2
[0075] Weight w1 = -1.2 (performance score)
[0076] Weight w2 = 0.8 (monthly absence days)
[0077] Weight w3 = -0.5 (satisfaction rating)
[0078] Weight w4 = 0.1 (department revenue)
[0079] Weight w5 = -0.3 (Project completion rate)
[0080] Employee ID9 standardized feature values:
[0081] Performance rating: 0.447
[0082] Monthly absence days: -0.293
[0083] Satisfaction rating: 0
[0084] Departmental revenue: 0.333
[0085] Project completion rate: 0.333
[0086] Calculate the linear combination (rounded to 3 decimal places): h(x) = 0.2 + (-1.2 × 0.447) + (0.8 × -0.293) + (-0.5 × 0) + (0.1 × 0.333) + (-0.3 × 0.333) ≈ 0.2 - 0.536 - 0.234 + 0 + 0.033 - 0.1 = -0.637
[0087] Predicted probability: He did not resign; the prediction was correct.
[0088] It should be noted that the candidate intelligent question-and-answer assessment includes: analyzing the logic and keyword matching degree of the candidate's answers through speech recognition and NLP, analyzing the candidate's emotional stability and communication ability by capturing facial expressions and body language through computer vision, and generating a question-and-answer score based on logic, keyword matching degree, emotional stability and communication ability.
[0089] Specifically, it includes the following:
[0090] During the data collection phase, the candidate's voice answers are collected using a microphone or recording device, and the candidate's face and upper body video are collected using a camera.
[0091] In the speech conversion stage: the number of words per minute is calculated, the frequency of repetitions and pauses (such as "um" and "ah") is detected, a speech emotion recognition model (such as OpenSmile feature extraction + classifier) is used to analyze positive / negative emotions in the tone, an API (such as iFlytek Hearing, Google Speech-to-Text) is used to convert the candidate's speech into text, and word vectors are used to calculate the similarity between the answer text and keywords.
[0092]
[0093] Weighted scoring assigns weights to different keywords (e.g., core competencies have higher weights);
[0094] Speech logic analysis: Dependency parsing (such as Stanford Parser) is used to determine sentence coherence. NLP models are used to detect causal words (such as "therefore" and "so") and transition words (such as "firstly" and "secondly") in the responses. Scoring rules are established, including the frequency of transition words and the completeness of logical chains (such as problem → analysis → solution).
[0095] Logicality = α × transition word score + β × structure score
[0096] Video facial expression analysis: ResNet-Emotion is used to recognize facial expressions captured by computer vision;
[0097] Emotional stability analysis: Calculate the standard deviation of facial expression changes (e.g., frequent switching between "surprise" and "neutrality" may indicate tension).
[0098]
[0099] Communication skills assessment: Eye contact: duration percentage (e.g., ≥60% is good), Gesture richness: number of effective gestures per minute (e.g., 2-3 times is moderate). Eye contact is assessed by detecting the direction of gaze (whether the eyes are looking at the camera) and blinking frequency (excessive blinking may indicate stress). Gesture richness is assessed by using OpenPose to detect the frequency of movement switching at key body points (e.g., shoulder and hand positions).
[0100] Example 2
[0101] refer to Figure 1 As shown, the present invention provides a system for implementing an artificial intelligence-based human resource management method, comprising: an intelligent recruitment module for receiving and screening resume information, and performing intelligent question-and-answer evaluation on the screened resume information;
[0102] The personnel management module is used to arrange the onboarding and training process based on the candidate evaluation results, and to record the performance, attendance, satisfaction, project completion rate and department revenue of the onboarded personnel.
[0103] The data analysis module is used to predict the turnover probability based on the performance, attendance and satisfaction of new employees using logistic regression, and to calculate the output per person and generate optimization suggestions by combining department revenue and project completion rate.
[0104] To further explain, the intelligent recruitment module includes: a job posting unit, used to create information on available positions;
[0105] The data collection unit is used to collect and filter job application resume information and industry-related knowledge semantics.
[0106] The intelligent consultation testing unit is used to build a question-and-answer knowledge base based on industry-related semantic knowledge, and to generate question-and-answer scores by recognizing the logic of candidates' answers and the degree of keyword matching.
[0107] The job matching unit is used to identify and extract key information from job applicant resumes. It uses NLP to automatically parse resumes in document format, including PDF and image formats, and extracts key information from the document information, including the candidate's skills, project experience, and educational background. The matching degree between the key information in the resume and the job information is calculated using cosine similarity.
[0108] The following is an example of cosine similarity calculation:
[0109] Let the resume vector be [0.2, 0.5, 0.3] (corresponding to the weights of skills, projects, and education);
[0110] Job vector: [0.4, 0.3, 0.3] (weights of job requirements, projects, and education)
[0111] calculate:
[0112] The Employment Contract Draft Unit is used to select and publish employment contract drafts based on the candidates' Q&A scores and the matching degree between key information in their resumes and the job postings.
[0113] The approval management unit is used to review and process draft employment contracts.
[0114] To further explain, the personnel management module includes: a personalized training unit that uses collaborative filtering algorithms to recommend training courses based on employee performance, skills assessment, and career goals;
[0115] Among them, reference Figure 3 As shown, the training course using collaborative filtering algorithm recommendation includes data collection, feature construction, scoring matrix, and application of collaborative filtering algorithm;
[0116] I. Data Collection: Collect data on performance, skills, career goals, and courses;
[0117] Performance: Performance ratings for the past 12 months (e.g., 0-5 points); Skills assessment: Skills matrix (e.g., ratings for programming skills, communication skills, etc.); Career goals: Employee's career direction for the next 1-3 years (e.g., "promote to team leader" or "transition to technical expert"); Course data includes course tags: Skill keywords for each course (e.g., "Python" or "project management"); Course difficulty: Graded according to skill requirements (beginner / intermediate / advanced); Course objectives: Corresponding job position or ability improvement direction (e.g., "team management" or "data analysis");
[0118] II. Feature construction, including user features and course features;
[0119] User characteristics include performance characteristics, skill characteristics, and career goal coding;
[0120] Course features include skill tag vectors and target matching degree;
[0121] Performance characteristics: Performance rating (normalized to 0-1);
[0122] Skill characteristics: Skill assessment vector (e.g., [programming ability = 0.8, communication ability = 0.5, project management = 0.3]);
[0123] Career goal coding: Transform goals into vectors (e.g., "Technical Expert" → [1,8,0], "Team Leader" → [0,1,0]).
[0124] Skill tag vector: Key words for skills covered by the course (e.g., [Python=1, Project Management=0]);
[0125] Goal matching degree: The similarity between the course and the career goal (e.g., the matching degree between the "Technical Expert" course and the goal is 0.9).
[0126] III. Constructing the scoring matrix;
[0127] Explicit ratings: Employee satisfaction ratings for past courses attended (if any);
[0128] Implicit rating: Implicit rating = α × skill matching degree + β × goal matching degree + γ × performance weight, where skill matching degree is the similarity between course skills and employee skill gaps;
[0129] Goal alignment: The similarity between course objectives and employee career goals;
[0130] Performance weighting: High-performing employees may be recommended for more advanced courses;
[0131] IV. Implementation process of collaborative filtering algorithm:
[0132] Step 1: Calculate user similarity: Use cosine similarity or Pearson correlation coefficient, combined with performance, skills, and target characteristics;
[0133] Similarity (u,v) = cos(feature vector) u eigenvectors v )
[0134] Step 2: Recommend courses based on user similarity: Find the K users who are most similar to the target user, and calculate the weighted average of their highly rated courses;
[0135]
[0136] Step 3: Calculate course similarity: based on course skill tags and target matching features;
[0137] Similarity (i,j) = cos(features of course i, features of course j)
[0138] Step 4: Recommend courses based on course similarity: Find similar courses that the target user has participated in historically, and recommend them in a weighted manner;
[0139]
[0140] Step 5, Hybrid Recommendation: Recommendations based on the similarity between users and courses;
[0141] Final rating = w × user recommendation rating + (1-w) × course recommendation rating
[0142] Where w is the weight.
[0143] Example as follows:
[0144] 1. Employee characteristics of employee A: performance 4.2, programming ability 0.7, communication ability 0.4, project management 0.2;
[0145] 2. Course Features: Course 1: Advanced Python (Skill Tag: Python = 1, Goal: Technical Expert), Course 2: Team Management (Skill Tag: Communication = 1, Goal: Team Leader), Course 3: Project Management (Skill Tag: Project Management = 1, Goal: Team Leader).
[0146] 3. Skills matching degree: Course 1: Programming ability matching degree = 0.7 → 0.7;
[0147] Course 2: Communication skills matching degree = 0.4 → 0.4;
[0148] Course 3: Project Management Fit = 0.2 → 0.2;
[0149] 4. Goal Matching Degree: Course 1: Goal Mismatch → 0;
[0150] Course 2: Perfect Match → 1;
[0151] Course 3: Partial Match → 0.8;
[0152] 5. Implicit scoring:
[0153] Implicit rating 2 = 0.4 × 0.4 + 1 × 1 + performance weight (4.2) × 0.3 ≈ 1.6
[0154] Implicit rating 3 = 0.2 × 0.2 + 0.8 × 1 + performance weight (4.2) × 0.3 ≈ 1.2
[0155] Final recommendation: Course 2 (Team Management) and Course 3 (Project Management).
[0156] The intelligent performance management unit is used to track the performance, attendance, satisfaction, project completion rate and department revenue of new employees in real time. It combines self-evaluation, superior rating and peer evaluation to generate a comprehensive score through a weighted algorithm.
[0157] The employee intelligent question-and-answer unit answers employee questions based on a question-and-answer knowledge base and NLP analysis.
[0158] To further explain, the data analysis module includes: a predictive analysis unit that uses logistic regression to predict employee performance trends based on new hires' performance, attendance, and satisfaction, provides early warnings of potential decline risks and turnover probabilities, and calculates per capita output and generates optimization suggestions by combining departmental revenue and project completion rates.
[0159] The data visualization unit displays key indicators such as recruitment information, employee turnover rate, and training effectiveness in real time.
[0160] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented using software plus a general-purpose hardware platform, or of course, using hardware. Based on this understanding, the above technical solutions, in essence or the parts that contribute to the related technology, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.
[0161] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the present 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 its spirit or basic characteristics. Therefore, the embodiments should be considered exemplary and non-limiting in all respects. The scope of the invention is defined by the appended claims rather than the foregoing description. Therefore, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention, and no reference numerals in the claims should be construed as limiting the scope of the claims.
[0162] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.
Claims
1. A human resource management method based on artificial intelligence, characterized in that, Includes the following steps: S1. In the recruitment screening stage, we collect and screen the resumes of applicants, conduct intelligent Q&A evaluation of the screened resumes, analyze the matching degree between the resumes and the job openings, and select to publish a draft employment contract based on the results of the intelligent Q&A evaluation and the matching degree. S2. During the onboarding management phase, the performance, attendance, satisfaction, project completion rate, and departmental revenue of new employees are recorded, and collaborative filtering algorithms are used to recommend training courses. S3. In the risk prediction stage, logistic regression is used to predict the turnover probability based on the performance, attendance and satisfaction of new employees. Combined with departmental revenue and project completion rate, per capita output is calculated and optimization suggestions are generated.
2. The artificial intelligence-based human resource management method according to claim 1, characterized in that, The candidate intelligent question-and-answer evaluation includes: analyzing the logic and keyword matching degree of the candidate's answers through speech recognition and NLP, analyzing the candidate's emotional stability and communication ability by capturing facial expressions and body language through computer vision, and generating a question-and-answer score based on logic, keyword matching degree, emotional stability and communication ability.
3. The artificial intelligence-based human resource management method according to claim 2, characterized in that, Recommending training courses using collaborative filtering algorithms involves the following steps: Step 1: Data Collection: Collect data on performance, skills, career goals, and courses; Step 2: Feature construction, including performance features, skill features, career goal codes, skill tag vectors, and goal matching degree; Step 3: Construct a rating matrix to determine explicit and implicit ratings; Step 4: Course Recommendation.
4. The artificial intelligence-based human resource management method according to claim 3, characterized in that, The per capita output is calculated as: department revenue / number of employees * project completion rate.
5. A system for implementing the artificial intelligence-based human resource management method according to any one of claims 1-4, characterized in that, include: The intelligent recruitment module is used to receive and screen resume information, and to conduct intelligent Q&A evaluation of the screened resumes. The personnel management module is used to arrange the onboarding and training process based on the candidate evaluation results, and to record the performance, attendance, satisfaction, project completion rate and department revenue of the onboarded personnel. The data analysis module is used to predict the turnover probability based on the performance, attendance and satisfaction of new employees using logistic regression, and to calculate the output per person and generate optimization suggestions by combining department revenue and project completion rate.
6. The artificial intelligence-based human resource management system according to claim 5, characterized in that, The intelligent recruitment module includes: a job posting unit, used to create job posting information; The data collection unit is used to collect and filter job application resume information and industry-related knowledge semantics. The intelligent consultation testing unit is used to build a question-and-answer knowledge base based on industry-related semantic knowledge, and to generate question-and-answer scores by recognizing the logic of candidates' answers and the degree of keyword matching. The job matching unit is used to identify and extract key information from applicant resumes and calculate the matching degree between the key information in the resume and the job information by using cosine similarity. The Employment Contract Draft Unit is used to select and publish employment contract drafts based on the candidates' Q&A scores and the matching degree between key information in their resumes and the job postings. The approval management unit is used to review and process draft employment contracts.
7. The artificial intelligence-based human resource management system according to claim 6, characterized in that, The personnel management module includes: Personalized training modules recommend training courses based on employee performance, skills assessment, and career goals using collaborative filtering algorithms; The intelligent performance management unit is used to track the performance, attendance, satisfaction, project completion rate and department revenue of new employees in real time. It combines self-evaluation, superior rating and peer evaluation to generate a comprehensive score through a weighted algorithm. The employee intelligent question-and-answer unit answers employee questions based on a question-and-answer knowledge base and NLP analysis.
8. The artificial intelligence-based human resource management system according to claim 7, characterized in that, The data analysis module includes: a predictive analysis unit, which uses logistic regression to predict employee performance trends based on the performance, attendance, and satisfaction of new employees, provides early warnings of potential decline risks and turnover probabilities, and calculates per capita output and generates optimization suggestions by combining departmental revenue and project completion rate; The data visualization unit displays key indicators such as recruitment information, employee turnover rate, and training effectiveness in real time.
9. The artificial intelligence-based human resource management system according to claim 8, characterized in that, The process of identifying and extracting key information from job application resumes includes: using NLP to automatically parse resumes in document format and extracting key information from the document information.
10. The artificial intelligence-based human resource management system according to claim 9, characterized in that, The key information includes the candidate's skills, project experience, and educational background.