Intelligent job-seeking assistant system based on artificial intelligence

Abstract

The invention relates to the technical field of artificial intelligence, and discloses an intelligent job-seeking assistant system based on artificial intelligence, and the system comprises a data collection and preprocessing module which carries out the comprehensive information collection of all mainstream recruitment websites, enterprise official websites and emerging industry vertical recruitment platforms through employing a network crawler technology, and carries out the processing of the information, meanwhile, information such as education background, work experience, skill specialty, occupational interest, project achievement details, special skill mastering degree and occupational development planning of job seekers is collected through an intelligent guide type input interface, and repeated, wrong and incomplete data are recognized and eliminated through an advanced data cleaning algorithm. According to the intelligent job-seeking assistant system, the intelligent job-seeking assistant system integrating multi-module cooperative work is constructed, all-around and personalized support for job seekers is achieved, the obvious technical effects and advantages are achieved, and in the aspect of post screening, the data collecting and preprocessing module widely collects multi-channel information and conducts deep processing, and a solid foundation is provided for follow-up analysis.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and more particularly to an intelligent job hunting assistant system based on artificial intelligence. Background Art

[0002] Today's job seekers face a series of severe challenges. First of all, it has become a major problem to accurately screen out suitable positions from a vast amount of recruitment information. According to incomplete statistics, the number of newly added job information on large recruitment platforms can reach tens of thousands per day, covering various industries and positions, and the complexity of the information is extremely high. Traditional job hunting service platforms simply list this recruitment information, and their matching methods mostly rely on basic keyword searches. For example, when a job seeker enters "software engineer" in the search bar, the platform can only display positions based on whether the keyword is included in the job title, and cannot deeply understand the specific skill requirements, work experience needs, and business field scope in the job description. This results in job seekers often having to spend a lot of time manually browsing the details of each position, comparing their own conditions with the job requirements one by one. Even so, many potential suitable positions may still be missed, and the screening efficiency is extremely low.

[0003] Secondly, it is also extremely difficult to create personalized job hunting materials. A high-quality resume and cover letter are crucial for job seekers to obtain interview opportunities. However, most job seekers lack professional resume-making skills and the ability to deeply analyze the target positions. They often use a one-size-fits-all resume template and cannot highlight their matching advantages with specific positions. For example, for a position that emphasizes teamwork and project management experience, a job seeker may not be able to elaborate in detail on their team role, achievements, and management methods used in past projects in the resume. When writing a cover letter, it is even more difficult to grasp the corporate culture style and the key points of the position requirements, resulting in empty content, lack of pertinence, and unable to effectively attract the attention of recruiters.

[0004] Furthermore, the interview preparation process also puts a lot of pressure on job seekers. The interview is a key link for enterprises to select talents, and the interview forms and focuses vary greatly for different industries and positions. Job seekers need to understand common interview questions, master effective answering skills, and be able to clearly demonstrate their abilities and advantages in the interview. However, currently, the channels for job seekers to obtain interview guidance are limited and of uneven quality. Most existing interview guidance materials or courses are mostly general templates and suggestions, and cannot provide personalized guidance according to the specific positions and personal characteristics of job seekers. For example, for positions in emerging industries, the interview questions may involve industry-leading technologies and innovative concepts, while traditional interview guidance materials often fail to update in time and cannot meet the needs of job seekers.

[0005] Although artificial intelligence technology has been gradually applied to the field of job hunting assistance, the existing systems have single functions and poor intelligence. Some existing job hunting systems that utilize artificial intelligence only perform simple algorithm optimization in the job matching process and still cannot solve core problems such as inaccurate information matching and lack of personalized guidance. For example, although some systems claim to use artificial intelligence algorithms for job recommendations, in actual applications, they still cannot understand the implicit requirements in job descriptions and cannot recommend based on the career development potential of job seekers. In terms of job application material generation, most systems can only provide simple template filling functions and cannot automatically generate high-quality and personalized content according to job requirements. In the interview simulation session, existing systems often can only provide limited interview questions and cannot conduct comprehensive and in-depth analysis and evaluation of job seekers' answers, making it difficult to truly help job seekers improve their interview skills.

[0006] In summary, there is an urgent practical need to develop an intelligent job hunting assistant system based on artificial intelligence that can comprehensively and efficiently solve various problems faced by job seekers during the job hunting process. Summary of the Invention

[0007] To overcome the above defects of the prior art, the present invention provides an intelligent job hunting assistant system based on artificial intelligence to solve the problems described in the above background technology.

[0008] The present invention provides the following technical solution: An intelligent job hunting assistant system based on artificial intelligence, characterized by comprising:

[0009] A data collection and preprocessing module, which uses web crawler technology to comprehensively collect information from major mainstream recruitment websites, corporate official websites, and emerging industry vertical recruitment platforms. At the same time, it collects information such as job seekers' educational backgrounds, work experiences, skills and specialties, career interests, project achievement details, special skill proficiency levels, and career development plans through an intelligent guided input interface. Advanced data cleaning algorithms are used to identify and eliminate duplicate, incorrect, and incomplete data, and natural language processing technology is used to perform in-depth standardization processing on text data, including unifying professional term specifications and clarifying the semantics of ambiguous expressions, thereby laying a data foundation for subsequent analysis.

[0010] The intelligent analysis and matching module integrates natural language processing technology, knowledge graph construction technology and deep learning algorithms. Natural language processing technology is used to perform deep semantic analysis of recruitment information and job seeker information to extract core key information and potential features; by building a knowledge graph, job requirements, skill systems, and industry knowledge are associated and integrated to understand the complex skills and quality requirements in job descriptions; a job matching model is built based on a deep learning algorithm. This model not only considers the job seeker's existing conditions, but also takes into account their career development potential and future job development needs, calculates an accurate matching score, and generates a detailed matching analysis report in a visual way, presenting the matching basis and potential gaps. In the intelligent analysis and matching module, in order to more accurately calculate the matching score, the following formula is introduced:

[0011]

[0012] Among them, M is the final matching score, n represents the number of key features extracted from recruitment information and job seeker information, wi is the weight of the i-th feature, which is set by the system through analysis of a large amount of historical data and the experience of domain experts, reflecting the importance of the feature for job matching, and si is the similarity score between the job seeker and the job requirements on the i-th feature, which is calculated through natural language processing and deep learning models.

[0013] The personalized job application materials generation module uses generative adversarial network (GAN) technology and text generation technology to automatically generate highly customized resumes and cover letters based on the matched job information and job seekers' personal information. It deeply analyzes the core skills, corporate culture characteristics and job requirements of the position, and integrates and highlights the corresponding experience and achievements of the job seekers. In the resume generation process, visual design technology is used to optimize the layout, and in the creation of cover letters, emotional analysis technology is used to write infectious and targeted content, so as to improve the pertinence and competitiveness of job application materials.

[0014] The interview simulation and coaching module builds an immersive interview simulation scenario based on virtual reality (VR) and augmented reality (AR) technology, and combines voice recognition, facial expression recognition, body language analysis technology and natural language processing technology to interact with job seekers in real time. Randomly extract questions from the dynamic interview question bank according to the type of position the job seeker is applying for, simulating the real interview pressure environment and emergencies. During the job seeker's answer process, the analysis is conducted from multiple dimensions such as language expression fluency, logical clarity, content relevance, emotional state, and self-confidence, and a detailed and practical evaluation report is generated, providing improvement suggestions such as recommending relevant training courses and simulated drill scenarios. In the interview simulation and coaching module, in order to comprehensively evaluate the job seeker's performance in the interview, an evaluation formula is introduced:

[0015] E = α×F + β×L + γ×C + δ×S

[0016] Among them, E is the comprehensive evaluation score. F represents the score for language expression fluency, which is obtained by analyzing factors such as the speaking speed and the number of pauses when the job seeker answers; L is the score for logical clarity, which is judged based on the organizational structure of the answer content and whether it focuses on the core of the question; C is the score for content relevance, which is determined according to the degree of relevance between the answer content and the job requirements; S represents the score for emotional attitude, which evaluates the confidence level, enthusiasm, etc. of the job seeker through facial expression recognition and tone analysis. α, β, γ, and δ are the weight coefficients corresponding to each score respectively, which are determined by professional interview evaluation criteria and a large amount of actual interview data training, and are used to balance the proportion of different evaluation dimensions in the comprehensive score.

[0017] The user interaction module adopts a design concept centered on user experience to create a simple, intuitive and powerful interaction interface. It uses intelligent voice interaction technology to facilitate job seekers to input personal information by voice and query job recommendation results. When presenting job recommendation results, in addition to showing key information and matching scores, it also uses data visualization technology to display information such as the development trend of the job in the industry and salary level comparison in the form of charts. After generating job application materials, it supports online real-time editing and preview, provides intelligent error correction and optimization suggestions. In the interview simulation session, it creates a realistic interview environment through VR / AR devices, displays simulated interview questions and user answers in real time, immediately shows a multi-dimensional evaluation report after the interview, and establishes a perfect user feedback mechanism. It uses machine learning technology to deeply analyze user feedback data and continuously optimize the system model and algorithm.

[0018] Furthermore, in the intelligent analysis and matching module, the deep learning neural network structure adopted by the job matching model contains multiple hidden layers. Through training with a large amount of historical data, the model can learn the complex and diverse matching patterns between recruitment positions and job seekers, so as to achieve accurate matching.

[0019] Furthermore, when the personalized job application material generation module generates a resume, it can automatically adjust the display order of experiences according to the job requirements, highlight key experiences, and at the same time use image recognition technology to optimize the inserted project achievement pictures to make their display effect better in the resume; when generating a cover letter, it generates an emotional tone suitable for the job based on sentiment analysis technology to enhance the text's appeal.

[0020] Furthermore, when the interview simulation and coaching module analyzes the answers of job seekers, it uses time series analysis technology to analyze the change in speaking speed of language expression to judge the coherence of thinking; it uses machine learning algorithms to model the relationship between body language and emotional state, so as to more accurately evaluate the emotional stability and confidence level of job seekers.

[0021] Further, the intelligent voice interaction technology in the user interaction module has the ability of multi-turn dialogue understanding and can intelligently ask questions when job seekers input incomplete information; the data visualization technology adopts a dynamic chart display method and updates the job-related data display in real time according to user operations, enhancing the user interaction experience.

[0022] Further, the data collection and preprocessing module can also use sentiment analysis technology to explore the corporate attitude and cultural atmosphere in recruitment information, providing more comprehensive corporate background information for job seekers; at the same time, when collecting job seeker information, it guides job seekers to supplement potential important information through intelligent Q&A to complete personal profiles.

[0023] Further, in the process of constructing the knowledge graph in the intelligent analysis and matching module, ontology reasoning technology is introduced, which can infer potential job skill requirements and job seeker development paths based on existing knowledge relationships, further enhancing the comprehensiveness and forward-looking of the matching, and providing more strategically significant job recommendations for job seekers.

[0024] Further, the interview simulation and coaching module can combine industry big data analysis to provide job seekers with success rate analysis for interviews in different industries and positions and frequency statistics of common interview questions, helping job seekers prepare for interviews more pertinently; and through the accumulation of simulated interview data, an interview case library is established to provide reference for subsequent job seekers.

[0025] Further, a running method of an intelligent job search assistant system based on artificial intelligence includes the following steps:

[0026] Start the data collection and preprocessing module, start the web crawler to collect recruitment information according to preset rules, and at the same time guide job seekers to input personal information, and sequentially perform cleaning, denoising and standardization processing on the collected data;

[0027] Transmit the processed data to the intelligent analysis and matching module, and use natural language processing technology, knowledge graph construction technology and deep learning algorithms for semantic parsing, feature extraction and matching calculation to generate a matching score and a matching analysis report;

[0028] According to the matching result, use generative adversarial networks and text generation technology in the personalized job search material generation module to generate customized resumes and cover letters;

[0029] In the interview simulation and coaching module, start the interview simulation scenario according to the job type selected by the job seeker, collect and analyze the answers of the job seeker, and generate an evaluation report and improvement suggestions;

[0030] Display various results through the user interaction module, receive user feedback, and use the feedback data to optimize the operation parameters and algorithms of each module.

[0031] Furthermore, a method for generating personalized job application materials for an intelligent job search assistant system based on artificial intelligence includes:

[0032] Obtain the matching job information and the personal information of the job seeker output by the intelligent analysis and matching module;

[0033] Screen out relevant achievements and experiences from the job seeker's experience data according to the core skills, corporate culture traits, and demand focus of the job;

[0034] Use visualization design technology to typeset and layout the resume in the format and style adapted to the job, highlighting key experiences;

[0035] Based on sentiment analysis technology, determine the sentiment tone matching the job, and write a cover letter content with strong appeal and pertinence;

[0036] Output highly customized resumes and cover letters for users to preview, edit, and download.

[0037] The technical effects and advantages of the present invention:

[0038] By constructing an intelligent job search assistant system integrating multi-module collaborative work, the present invention realizes all-round and personalized support for job seekers, with remarkable technical effects and advantages.

[0039] In terms of job screening, the data collection and preprocessing module widely collects information from multiple channels and deeply processes it, providing a solid foundation for subsequent analysis. The intelligent analysis and matching module integrates a variety of advanced technologies, can deeply understand recruitment information and job seeker information, and not only based on existing conditions, but also combines career development potential and future job requirements for accurate matching, greatly improving the accuracy and comprehensiveness of job recommendations, helping job seekers quickly locate potential suitable jobs and saving a large amount of screening time.

[0040] In the generation of job application materials, the personalized job application material generation module uses generative adversarial networks and text generation technology to automatically generate highly customized resumes and cover letters for each matching job. By deeply analyzing job key points, highlighting relevant experiences of job seekers, and using visualization design and sentiment analysis technology, the pertinence and competitiveness of job application materials are significantly improved, increasing the probability of job seekers obtaining interview opportunities.

[0041] For the interview preparation stage, the interview simulation and coaching module constructs immersive scenarios based on VR and AR technologies, combines various recognition and processing technologies, and comprehensively and deeply analyzes job seekers' answers from multiple dimensions. The generated detailed evaluation reports and specific improvement suggestions can effectively help job seekers improve their interview skills. At the same time, combined with the interview success rate and common problem statistics provided by industry big data analysis, as well as the established interview case library, it further helps job seekers prepare for interviews more targeted.

[0042] In terms of user interaction experience, the user interaction module adheres to the core design concept of user experience, adopts technologies such as intelligent voice interaction and dynamic data visualization, facilitates job seekers' operations, provides rich information display, supports online editing and preview of job application materials, presents the interview simulation situation in real time, and establishes an effective feedback mechanism. Through in-depth analysis of feedback data by machine learning technology, the system model and algorithm are continuously optimized, enabling the system to continuously adapt to market changes and user needs, and providing increasingly high-quality services for job seekers.

[0043] In summary, the intelligent job hunting assistant system based on artificial intelligence of the present invention comprehensively and efficiently solves many problems faced by job seekers during the job hunting process, significantly improves the job hunting efficiency and success rate, and has extremely high practical value and innovation. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is the system architecture diagram of the present invention; DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0046] It can be understood that the terms "first", "second", etc. used in the present application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish one element from another.

[0047] In order to enable those skilled in the art to better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0048] Embodiment:

[0049] The present invention provides an intelligent job hunting assistant system based on artificial intelligence, which is characterized by including:

[0050] A data collection and preprocessing module. In the data collection stage, according to carefully set rules, web crawlers regularly conduct in-depth crawling on major mainstream recruitment websites (such as BOSS Zhipin, 51job, Zhaopin.com, etc.), enterprise official websites (covering enterprises of various scales and industries), and emerging industry vertical recruitment platforms (such as recruitment platforms focusing on emerging fields such as artificial intelligence and blockchain). To ensure the timeliness and comprehensiveness of data, web crawlers will monitor page updates in real time, and once new recruitment information is released, they will immediately collect it. At the same time, on the user interaction interface, an intelligent guided input form is designed to guide job seekers to fill in personal information in a concise and clear manner. For example, for educational background, not only is it required to fill in the school name, major, enrollment and graduation time, but also through drop-down menus and prompt messages, job seekers are guided to supplement details such as relevant course grades and academic honors obtained; in the work experience section, specific responsibilities of each job position, key achievements obtained, details of involved projects, etc. will be asked in detail to ensure rich and valuable information is collected.

[0051] The collected data enters the preprocessing link. First, an advanced data cleaning algorithm is adopted. By setting a series of data filtering rules, duplicate data is identified and removed. For example, by comparing key contents such as the title of recruitment information, job description, and enterprise information, identical recruitment entries are removed; for incorrect data, a data verification algorithm is used to check whether the data format is correct, such as whether the date format conforms to the specification and whether there are abnormal values in the numbers. Data that does not meet the requirements is marked and corrected; for incomplete data, missing values are predicted through a data analysis model, or prompts for supplementary information are sent to job seekers. Then, natural language processing technology is used to conduct in-depth standardization processing on the text data. For example, for professional terms, a term library is established to standardize different expressions of the same professional term into a standard form; for fuzzy expressions, semantic clarification is carried out through semantic analysis and context understanding. For example, "having a certain programming ability" is clarified into specific programming languages and proficiency levels.

[0052] The intelligent analysis and matching module inputs the preprocessed recruitment information and job seeker information into the natural language processing model. For recruitment information, lexical analysis, syntactic analysis, and semantic analysis techniques are used to extract key information such as job titles, job responsibilities, and job requirements. For example, from the job description "Responsible for developing enterprise-level application systems based on Java, with more than 3 years of Java development experience and familiarity with the Spring framework", key skills and experience requirements such as "Java development", "Spring framework", and "3 years of experience" are accurately extracted. For job seeker information, natural language processing techniques are also used to extract features such as core professional courses, academic performance rankings in the educational background, project names, roles played, and achievements obtained in the work experience.

[0053] Subsequently, the job requirements, skill system, and industry knowledge are associated and integrated by constructing a knowledge graph. For example, connections are established between the skill of "Java development" and knowledge nodes such as relevant programming language features, common frameworks, and industry application scenarios to form an organic knowledge network, thereby deeply understanding the complex skill and quality requirements in the job description. Then, these features are input into a pre-trained job matching model. This model adopts a neural network structure in deep learning, including multiple hidden layers, and is trained through a large amount of historical data to learn the matching patterns between recruitment positions and job seekers. During the training process, the weights and thresholds of the neural network are continuously adjusted to enable the model to accurately capture the complex relationships between different features. When calculating the matching degree score, according to the formula:

[0054]

[0055] The system will dynamically adjust the weight w of each feature based on the analysis of a large amount of historical data and the experience of domain experts. i . For example, for technical positions, the weight of professional skill features will be relatively high; for management positions, features such as leadership ability and teamwork experience will be more prominent. At the same time, through natural language processing and deep learning models, the similarity score s of the job seeker with the job requirements on the i-th feature is calculated. i Finally, an accurate matching degree score is obtained, and job seekers are recommended positions in descending order of the score. At the same time, a detailed matching analysis report is generated to visually present the matching basis and potential gaps, such as showing the comparison between the job seeker and the job requirements on each key feature in the form of a chart.

[0056] Personalized job application material generation module: When the system recommends a suitable position for a job seeker, the personalized job application material generation module starts to work. First, it deeply analyzes the core skills, corporate culture characteristics, and key aspects of the position requirements. For example, for a product manager position recruited by an Internet company that emphasizes an innovative culture, the system will analyze core skills such as innovative thinking, user needs insight, project management, and key aspects of requirements such as the recognition of the innovative culture. Then, relevant achievements and experiences are screened out from the job seeker's experience data. For instance, if the job seeker has participated in an innovative product optimization project, the system will extract detailed information about the project, including the project goals, innovative measures taken, and achievements obtained.

[0057] When generating a resume, visual design technology is used to typeset and layout the resume in a format and style suitable for the position. For example, for technical positions, the technical skills section is placed in a prominent position and presented in a simple and clear table form to show the programming languages, tools, etc. mastered; for creative design positions, a more creative and visually appealing typesetting is adopted to highlight the work display section. At the same time, the display order of experiences is automatically adjusted according to the position requirements to highlight key experiences, and image recognition technology is used to optimize the inserted project achievement pictures, such as adjusting the picture size and clarity to make their display effect better in the resume. When generating a cover letter, based on sentiment analysis technology, the emotional tone matching the position is determined. For example, for a position in a dynamic startup company, the emotional tone of the cover letter can be set as enthusiastic, positive, and full of drive. Then, around the matching points between the job seeker and the position, inspiring and targeted content is written to emphasize the job seeker's enthusiasm and suitability for the position, such as elaborating on the abilities and characteristics demonstrated in past projects that match the position requirements.

[0058] Interview Simulation and Coaching Module: When a job seeker chooses to conduct an interview simulation, the system randomly selects questions from the dynamic interview question bank according to the type of position they applied for. The dynamic interview question bank is continuously updated based on industry development, changes in enterprise requirements, and user feedback to ensure the timeliness and pertinence of the questions. For example, for emerging artificial intelligence algorithm positions, questions about the latest algorithm models and application case analyses will be added to the question bank in a timely manner. The speech recognition technology is used to convert the job seeker's spoken answers into text, and then the natural language processing technology is used to analyze the content of the answers. When analyzing the fluency of language expression, the time series analysis technology is used to analyze the speed changes of language expression to judge the coherence of thinking, such as whether the speed is stable and whether the pauses are reasonable; when judging the logical clarity, it is evaluated through syntactic analysis and semantic understanding technology according to the organizational structure of the answer content and whether it focuses on the core of the question; for content relevance, it is determined by using a text matching algorithm based on the degree of association between the answer content and the position requirements; when evaluating the emotional attitude, the facial expression recognition technology is used to identify the job seeker's facial expressions, such as smiling and frowning, and combined with the tone analysis technology to judge their confidence level, enthusiasm, etc. According to the analysis results, a comprehensive evaluation score is generated according to the formula: E = α×F + β×L + γ×C + δ×S. Among them, α, β, γ, and δ are the weight coefficients corresponding to each score item, which are dynamically adjusted according to professional interview evaluation criteria and a large amount of actual interview data training. For example, for sales positions, the weights of language expression fluency and emotional attitude may be relatively high; for technology R & D positions, the weights of content relevance and logical clarity are more prominent. The system generates a detailed and practical evaluation report based on the evaluation results, providing improvement suggestions such as recommending relevant training courses and simulation scenarios. For example, if the evaluation report shows that the job seeker has deficiencies in logical clarity, the system will recommend relevant logical thinking training courses and targeted simulation scenarios to help the job seeker improve their interview ability.

[0059] User Interaction Module: The user interaction module adopts a simple and intuitive interface design to facilitate the operation of job seekers. In the stage of inputting personal information, using intelligent voice interaction technology, job seekers can input their personal information by voice. The system has the ability to understand multi-round conversations and can conduct intelligent follow-up questions when the job seeker inputs incomplete information. For example, when the job seeker voices "Graduated from XX University with a bachelor's degree", the system will automatically ask "What is your major?". When presenting the job recommendation results, in addition to presenting key information and matching scores, data visualization technology is also used to display information such as the development trend of the position in the industry and salary level comparison in the form of charts. For example, a bar chart is used to show the average salary levels of the same type of positions in different industries, and a line chart is used to show the demand change trend of this position in the past few years.

[0060] After generating job application materials, it supports online real-time editing and preview, and provides intelligent error correction and optimization suggestions. For example, when a job seeker enters incorrect grammar or formatting issues while editing a resume, the system will promptly pop up a prompt box for error correction; for the resume content, the system will provide optimization suggestions such as highlighting key experiences and optimizing language expressions according to the job matching degree and industry general specifications. In the interview simulation session, a realistic interview environment is created through VR / AR devices, and the simulated interview questions and user answers are displayed in real time. After the interview ends, a multi-dimensional evaluation report is immediately shown. Users can give feedback on the jobs recommended by the system, the generated job application materials, and the interview simulation results through the feedback button. The system collects the user feedback data and uses machine learning technology to deeply analyze the user feedback data to continuously optimize the system model and algorithm. For example, if a large number of users feedback that a certain job recommendation is inaccurate, the system will re-evaluate the matching algorithm and data of this job and conduct targeted optimization to continuously improve the service quality and user experience of the system.

[0061] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0062] The above embodiments only represent several implementation manners of the present invention. Their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the invention patent should be subject to the appended claims.

[0063] The above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An intelligent job hunting assistant system based on artificial intelligence, characterized in that: Including: Data collection and preprocessing module: Use web crawlers to collect recruitment information from multiple platforms, collect diverse information of job seekers through intelligent interfaces, clean and denoise data through advanced algorithms, standardize texts with natural language processing technology, and lay a foundation for analysis; Intelligent analysis and matching module: Integrate natural language processing, knowledge graph construction, and deep learning algorithms to analyze recruitment and job-seeking information, construct a knowledge graph to understand job requirements, build a job matching model to comprehensively consider conditions and potential, calculate the matching degree, and generate a visual analysis report. In the intelligent analysis and matching module, in order to calculate the matching degree score more accurately, the following formula is introduced: Where M is the final matching degree score, n represents the number of key features extracted from recruitment information and job seeker information, wi is the weight of the i-th feature, and this weight is set by the system through the analysis of a large amount of historical data and the experience of domain experts, reflecting the importance of this feature for job matching. si is the similarity score of the job seeker on the i-th feature compared with the job requirements, which is calculated through natural language processing and deep learning models; Personalized job application material generation module: With the help of generative adversarial networks and text generation technology, generate customized resumes and cover letters according to job requirements and personal information, analyze job key points to highlight the job seeker's experience, and use visualization and sentiment analysis technology to improve the quality of materials; Interview simulation and coaching module: Build an immersive interview scenario based on VR and AR technologies, combine various recognition and processing technologies for real-time interaction, analyze job seekers' answers from multiple dimensions, generate evaluation reports and improvement suggestions. In the interview simulation and coaching module, in order to comprehensively evaluate job seekers' performance in the interview, an evaluation formula is introduced: E = α×F + β×L + γ×C + δ×S Where E is the comprehensive evaluation score, F represents the score of language expression fluency, which is obtained by analyzing the speaking speed, number of pauses, etc. of job seekers when answering; L is the score of logical clarity, which is judged according to the structure of the answer content and whether it focuses on the core of the question; C is the score of content relevance, which is determined according to the relevance of the answer content to job requirements; S represents the score of emotional attitude, which evaluates the confidence, enthusiasm, etc. of job seekers through facial expression recognition and tone analysis. α, β, γ, δ are the weight coefficients corresponding to each score respectively, which are determined by professional interview evaluation criteria and a large amount of actual interview data training, and are used to balance the proportion of different evaluation dimensions in the comprehensive score; User interaction module: Build a simple interface based on the concept of user experience, support voice interaction, display job information and charts, provide functions for editing and previewing job application materials and interview simulation, and establish a feedback mechanism to optimize the system.

2. The intelligent job hunting assistant system based on artificial intelligence according to claim 1, characterized in that: The job matching model of the intelligent analysis and matching module adopts a deep learning neural network with multiple hidden layers, and realizes accurate matching through training with a large amount of data.

3. An intelligent job hunting assistant system based on artificial intelligence according to claim 1, characterized in that: When generating a resume, the personalized job application material generation module can adjust the experience order according to the job requirements and optimize the picture display. When generating a cover letter, it can determine the appropriate emotional tone to enhance the appeal.

4. An intelligent job hunting assistant system based on artificial intelligence according to claim 1, characterized in that: The interview simulation and coaching module uses time series analysis for speaking speed and machine learning algorithms to analyze the relationship between body language and emotions, and evaluate the state of job seekers.

5. The intelligent job hunting assistant system based on artificial intelligence according to claim 1, characterized in that: The intelligent voice interaction of the user interaction module has the ability of multi-round dialogue, and data visualization uses dynamic charts to enhance the interaction experience.

6. The intelligent job hunting assistant system based on artificial intelligence according to claim 1, characterized in that: The data collection and preprocessing module can utilize sentiment analysis to explore the corporate atmosphere and improve job seeker profiles through intelligent Q&A.

7. An intelligent job hunting assistant system based on artificial intelligence according to claim 1, characterized in that: The intelligent analysis and matching module introduces ontology reasoning technology in knowledge graph construction to reason about job skill requirements and job seeker development paths.

8. An intelligent job hunting assistant system based on artificial intelligence according to claim 1, characterized in that: The interview simulation and coaching module combines industry big data to provide statistics on interview success rates and common questions, and establishes a case library for reference.

9. A running method of an intelligent job hunting assistant system based on artificial intelligence, characterized in that, Including: Start the data collection and preprocessing module to collect and process data; Transmit the data to the intelligent analysis and matching module for parsing and matching; generate materials in the personalized job application material generation module according to the matching results; start scenario analysis and answers in the interview simulation and coaching module; Display the results through the user interaction module and collect feedback to optimize the system.

10. A personalized job application material generation method for an intelligent job hunting assistant system based on artificial intelligence, characterized in that, Including: Obtain information on matching positions and job seekers; Screen relevant experience and achievements; Use visualization technology to typeset the resume and write a cover letter based on sentiment analysis; Output customized materials for users to process.

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