CN116579755A - Personnel data distribution system for correspondingly issuing recruitment information according to delivery area - Google Patents
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Abstract
The invention relates to the technical field of recruitment management. The invention relates to a personnel data distribution system for correspondingly issuing recruitment information according to a delivery area. The system comprises an information collection unit, an information classification unit, a talent recommendation unit and a cloud storage unit; when the market recruits, the information collecting unit stores the applied job seeker information and recruiter information, and checks and verifies the collected job seeker information and recruiter information; through analyzing the different dimensionalities of job seekers and job positions simultaneously, the job positions required by job seekers and the job position required person requirements are accurately acquired, matching accuracy is improved, false information is reduced to enter the cloud end through verification detection of acquired user information, meanwhile, the situation that the user cannot acquire the required positions and talents in time due to false information interference is avoided, the job position entering is automatically applied for a high-end user through the mode, job entering failure reasons are summarized, and job entering matching information is continuously updated.
Description
Technical Field
The invention relates to the technical field of recruitment management, in particular to a personnel data distribution system for correspondingly issuing recruitment information according to a delivery area.
Background
Most of the existing recruitment websites only provide search services of job position publishing and job seeker information, but due to the fact that the enterprise throwing area and the job seeker search area are different, some target users cannot be accurately matched with proper job position information, meanwhile, recruitment effects of the enterprise are affected, meanwhile, when user information is collected by the recruitment websites, the user information cannot be judged, in the recruitment process, false information is flooded, the user using effect is affected, in addition, the user is slower in job position matching moderate speed through self information analysis, the job entering efficiency is low, meanwhile, the talent speed of the enterprise recruitment is affected, and a personnel data distribution system for correspondingly publishing the recruitment information according to the throwing area is provided.
Disclosure of Invention
The invention aims to provide a personnel data distribution system for correspondingly issuing recruitment information according to a delivery area so as to solve the problems in the background technology.
In order to achieve the above purpose, the personnel data distribution system for correspondingly issuing recruitment information according to the delivery area comprises an information collection unit, an information classification unit, a talent recommendation unit and a cloud storage unit;
the information collecting unit sends information to the job seeker information and recruiter information to fill in a collecting table, checks and verifies the collected job seeker information and recruiter information, and judges authenticity of the job seeker and recruiter according to a verification result;
the information classification unit is used for analyzing according to the user information registered by the information collection unit, acquiring the demand information of the job seeker and the recruiter according to the analysis data, and providing an information display channel according to the demand information similarity;
the talent recommending unit is used for collecting the information of the job seeker in the information classifying unit, analyzing the information of the job seeker, obtaining high-end talent data, inquiring and evaluating the adapted recruiter information according to a matching algorithm according to the high-end talent data, and sending a job seeker application to the recruiter according to an evaluation result;
the cloud storage unit is used for analyzing and uploading the results of the job application sent by the talent recommending unit, updating the results by combining with the existing job application process, and updating the sending route for the talent recommending unit according to the latest cloud data.
As a further improvement of the technical scheme, the information collecting unit comprises an information registering module and an information analyzing module;
the information registration module establishes a cloud end, and transmits the requirement intention filled in by the job seeker and the recruiter to enter the cloud end;
the information analysis module is used for auditing the information collected by the cloud and eliminating invalid false information.
As a further improvement of the technical scheme, the information registration module comprises a personal information collection module and an enterprise information collection module;
the personal information collection module provides information filling form for the job seeker, so that relevant information of the job seeker is collected and sent to the cloud;
the enterprise information collection module provides information filling form for recruiters, so that information related to the recruiters is collected and sent to the cloud.
As a further improvement of the technical scheme, the information analysis module comprises a personal analysis module and an enterprise analysis module;
the personal analysis module sends relevant information of the job seeker in the cloud to the personal information collection module, judges false information of the information, and then retains the information in the cloud according to the judging result;
the personal analysis module sends the recruiter related information of the cloud to the personal information collection module, carries out false information judgment on the information, and then reserves the information in the cloud according to the judgment result.
The matching algorithm specifically comprises the following steps:
extracting talent parameter characteristics according to high-end talent data, and forming talent parameter vectors according to the talent parameter characteristics;
extracting recruitment parameter features according to recruiter information, and forming recruitment parameter vectors according to the recruitment parameter features;
the Manhattan distance formula is adopted to calculate the Manhattan distance between the talent parameter vector and the recruitment parameter vector, so that the matching is realized; the Manhattan distance formula isD：
，
wherein ,Xis a vector of the talent parameters,Yand recruiting the parameter vector.
As a further improvement of the technical scheme, the information classification unit comprises a data storage module and a data analysis module;
the data storage module is used for carrying out characteristic analysis on the job seeker information and the recruiter information and classifying the information according to analysis characteristic results;
and the data analysis module searches for matching in the cloud according to requirements of job seekers and recruiters, and provides information recommendation according to matching results.
As a further improvement of the technical scheme, the data storage module comprises a personal classification module and an enterprise classification module;
the personal classification module collects and analyzes the position requirements of the job seeker, and performs step classification according to the analysis result and different position requirements of the job seeker;
the enterprise classification module collects and analyzes talent requirements required by recruiters, and classifies the talent requirements according to analysis results and talent qualification required by the recruiters.
As a further improvement of the technical scheme, the data analysis module comprises a personal demand module and an enterprise demand module;
the personal demand module is used for evaluating by combining the related information of the job seeker collected by the personal information collection module and the analysis result of the personal classification module, and searching similar recruiter information in the cloud according to the evaluation result for recommendation;
the enterprise demand module is used for evaluating the recruiter related information collected by the enterprise information collection module and the analysis result of the enterprise classification module, and similar job seeker information is searched in the cloud for recommendation according to the evaluation result.
As a further improvement of the technical scheme, the talent recommending unit comprises a talent analyzing module and an job entering analyzing module;
the talent analysis module extracts job seekers with high evaluation coefficients in the evaluation results of the personal demand module, and performs characteristic collection and evaluation on the job seekers;
the job entering analysis module searches the recruiter information with accurate matching in the cloud according to the characteristic acquisition result and the evaluation result of the talent analysis module, analyzes the job entering condition of the positions required by the matched recruiter information, performs job entering judgment by combining the analysis result with the matched information related to the job seeker, and sends a job entering application to the recruiter or secondary matching corresponding recruiter information according to the judgment result.
As a further improvement of the technical scheme, the cloud storage unit comprises an information analysis module and a data updating module;
the information analysis module collects the job-seeking failure process of the job-seeking analysis module, evaluates the job-seeking failure process, and extracts failure information caused by fine mismatch from the job-seeking failure process to analyze;
the data updating module is used for uploading the analysis result of the data updating module to the cloud, updating information for the cloud to enable the subsequent job entering analysis module to carry out job entering judgment for the job seeker matching position.
Compared with the prior art, the invention has the beneficial effects that:
in the personnel data distribution system for correspondingly issuing recruitment information according to the delivery area, different dimensionalities of the job seeker and the job position are analyzed simultaneously, so that the job position required by the job seeker and the job position required by the job position are accurately acquired, the matching accuracy is improved, false information is reduced to enter the cloud end through checking and detecting the acquired user information, the situation that the user cannot acquire the required position and talent in time due to false information interference is avoided, the job position is automatically applied to a high-end user through the fact that the job position is acquired, the failure reason of job entry is summarized, the job entry matching information is continuously updated, and more efficient and accurate recruitment service is provided for the recruiter and the job seeker.
Drawings
FIG. 1 is an overall flow diagram of the present invention;
FIG. 2 is a flow chart of demand intent delivery of the present invention;
FIG. 3 is a flow chart of the present invention for removing invalid false information;
FIG. 4 is a flow chart of classifying information according to the analysis characteristic result of the present invention;
FIG. 5 is a flow chart of providing information recommendation for matching results according to the present invention;
FIG. 6 is a block flow diagram of job seeker related information of the present invention for performing job entering judgment;
fig. 7 is a flow chart of updating information for a cloud according to the present invention.
The meaning of each reference sign in the figure is:
1. an information collection unit; 2. an information classification unit;
10. an information registration module; 11. a personal information collection module; 12. an enterprise information collection module;
20. an information analysis module; 21. a personal analysis module; 22. an enterprise analysis module;
30. a data storage module; 31. a personal classification module; 32. an enterprise classification module;
40. a data analysis module; 41. a personal demand module; 42. an enterprise demand module;
50. talent recommending unit; 51. talent analysis module; 52. job entry analysis module;
60. a cloud storage unit; 61. an analysis learning module; 62. and a data updating module.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1:
referring to fig. 1-7, the present embodiment is directed to providing a personnel data distribution system for corresponding distribution of recruitment information according to a delivery area, which includes an information collecting unit 1, an information classifying unit 2, a talent recommending unit 50 and a cloud storage unit 60;
the information collecting unit 1 sends information to the job seeker information and recruiter information to fill in a collecting table, checks and verifies the collected job seeker information and recruiter information, and judges authenticity of the job seeker and recruiter according to a verification result;
the information classification unit 2 is used for analyzing according to the user information registered by the information collection unit 1, acquiring the demand information of the job seeker and the recruiter according to the analysis data, and providing an information display channel according to the demand information similarity;
the talent recommending unit 50 is used for collecting the information of the job seeker in the information classifying unit 2, analyzing the information of the job seeker, obtaining high-end talent data, inquiring and evaluating the adapted recruiter information by utilizing a matching algorithm according to the high-end talent data, and sending a job seeker application to the recruiter according to an evaluation result;
the matching algorithm specifically comprises the following steps:
extracting talent parameter characteristics according to high-end talent data, and forming talent parameter vectors according to the talent parameter characteristics;
extracting recruitment parameter features according to recruiter information, and forming recruitment parameter vectors according to the recruitment parameter features;
the Manhattan distance formula is adopted to calculate the Manhattan distance between the talent parameter vector and the recruitment parameter vector, so that the matching is realized; the Manhattan distance formula isD：
，
wherein ,Xis a vector of the talent parameters,Yand recruiting the parameter vector. The Manhattan distance minimum is the matched adapted recruiter information.
The cloud storage unit 60 is configured to analyze and upload the result of the job application sent by the talent recommendation unit 50, update the result by combining with the existing job application process, and update the sending route for the talent recommendation unit 50 according to the latest cloud data.
The information collection unit 1 includes an information registration module 10 and an information analysis module 20;
the information registration module 10 establishes a cloud end, and transmits the requirement intention filled in by the job seeker and the recruiter to the cloud end;
the information analysis module 20 is used for auditing the information collected by the cloud, eliminating invalid false information and improving information accuracy.
The information registration module 10 includes a personal information collection module 11 and an enterprise information collection module 12;
the personal information collection module 11 provides an information filling form for the job seeker, so that relevant information of the job seeker is collected and sent to the cloud, and a user can access a personnel data distribution system official network for recruitment information release through the Internet to fill in personal or enterprise information for registration;
the enterprise information collection module 12 provides information filling tables for recruiters, so that relevant information of the recruiters is collected and sent to the cloud, a user logs in the system, fills in the recruitment information according to relevant rules, and submits a release request after the recruiters pass the verification.
The information analysis module 20 includes a personal analysis module 21 and an enterprise analysis module 22;
the personal analysis module 21 sends the relevant information of the job seeker in the cloud to the personal information collection module 11, judges false information of the information, and then retains the information in the cloud according to the judging result;
the personal analysis module 21 sends the recruiter related information of the cloud to the personal information collection module 11, judges false information of the information, and then retains the information in the cloud according to the judging result, verifies the authenticity of the information of the job seeker and the recruiter and performs scoring by combining user feedback. For information provided by job seekers and recruiters, such as mailboxes, telephones, enterprise registration certificates and the like, whether the information is credible or not is judged by verifying and comparing a public database and a well-known third party data mechanism. If there is a doubt, more evidence of authenticity may be obtained by means of a field survey or an on-line questionnaire, etc. Meanwhile, the user feedback mechanism is combined to score the job seeker and the recruiter, and the credibility of the job seeker and the recruiter is evaluated by analyzing by means of big data technology and the like. For example, for a job seeker, a score may be given based on what they have performed in the past, educational background, certificates and reputation that were obtained, etc. reflecting their performance and quality.
Based on natural language processing technology, key information in resume and enterprise information is screened and analyzed. And analyzing key words, phrases and sentences contained in resume or enterprise recruitment information delivered by job seekers by adopting a natural language processing technology, and judging whether false or exaggerated bias language of the words exists or not by assisting methods such as text emotion analysis and the like, so as to judge and analyze the authenticity of the information.
The information classification unit 2 includes a data storage module 30 and a data analysis module 40;
the data storage module 30 is used for performing feature analysis on the job seeker information and the recruiter information, and classifying the information according to the analysis feature result;
the data analysis module 40 performs searching for a match in the cloud according to the demands of the job seeker and the recruiter, and provides information recommendation according to the matching result.
The data storage module 30 includes a person classification module 31 and an enterprise classification module 32;
the personal classification module 31 collects and analyzes the position requirements of the job seeker, performs step classification according to the analysis result according to the different position requirements of the job seeker, and collects and analyzes the talent requirements of the recruiter. Related information required by talents required by recruiters is acquired by adopting modes such as crawlers, social media and the like, and multidimensional and deviation rate analysis is performed. The recruiter's required qualification can be analyzed according to the human resource disclosure information by referring to information such as a list on the recruitment website, the enterprise's required talent requirements, and the industry data.
Classifying according to the analysis result and the talent qualification required by recruiters. And matching the recruiter into corresponding talent classifications according to the qualification requirements of talents required by the recruiter. Such as high skill talents, excellent management talents, emerging domain talents, etc., to classify successfully matched talents into corresponding qualification classifications. For each talent qualification classification, the fine classification and labeling can be further carried out according to factors such as working experience, job seeker type and the like;
the enterprise classification module 32 collects and analyzes talent requirements of recruiters, classifies the talent requirements according to analysis results, and collects and analyzes job requirements of job seekers. Firstly, related information of job requirements required by job seekers is acquired through web crawlers, social media and other modes, and multidimensional analysis is carried out. The text can be subjected to word segmentation, emotion recognition, theme extraction and other treatments by adopting a natural language processing technology, the actual position requirements and the actual job seeker requirements are mined, and the information is quantized into key indexes.
And performing step classification according to different job requirements of job seekers according to analysis results. And step classification is carried out on the job seeker according to the collected and analyzed job position requirements. Such as advanced management posts, technical posts, sales posts, market posts, etc., the requirements of different job types are placed into corresponding job classifications, respectively. For each job classification, the fine classification and labeling can be further performed according to factors such as experience, salary level and the like.
The data analysis module 40 includes a personal demand module 41 and an enterprise demand module 42;
the personal demand module 41 evaluates the relevant information of the job seeker collected by the personal information collection module 11 in combination with the analysis result of the personal classification module 31, searches similar recruiter information in the cloud for recommendation according to the evaluation result, collects the information of the job seeker, matches the position requirements and enterprise demands, and recommends positions meeting the capabilities and interests of the job seeker by using a machine learning algorithm.
A feedback mechanism is introduced, and recommendation strategies are continuously adjusted and optimized from two angles of a job seeker and a human unit, so that a better matching effect is achieved. Matching and analyzing keywords contained in job seekers and job seekers resume by using a natural language processing technology, particularly checking and integrating related skills and experiences, and recommending a job list which is most in line with job seekers' ability and personal background;
the enterprise demand module 42 evaluates the recruiter-related information collected by the enterprise information collection module 12 in combination with the analysis result of the enterprise classification module 32, and searches similar job seeker information in the cloud for recommendation according to the evaluation result;
searching talents suitable for the demands of enterprises by adopting various ways such as talent libraries, crowd-sourcing and recruitment websites, obtaining related information such as resume and wilful experiences, and screening talent pools meeting the demands of posts;
analyzing the resume and the enterprise demand conditions by using a natural language processing technology, combining important keyword coefficients and requirements into an index combination, establishing a resume library, screening and grading job seekers in the talent library for matching;
based on a big data analysis technology, talent groups meeting the requirement of personnel units are mined from massive candidate talents by using a data modeling method, the range is narrowed, and finally, matching of personnel selection is provided.
The talent recommendation unit 50 includes a talent analysis module 51 and an job entry analysis module 52;
the talent analysis module 51 extracts job seekers with high evaluation coefficients in the evaluation results of the personal demand module 41, and performs characteristic collection and evaluation on the job seekers, and the talents are required to be analyzed firstly, including contents in aspects of skills, experience, professional preference and the like;
then, screening the recruitment company, and matching proper positions and working contents according to talent information and requirements of the recruitment company;
finally, providing a corresponding technical scheme according to the working content, and helping talents adapt to the work better;
the talents and recruitment companies are analyzed and matched by natural language processing, machine learning, deep learning and other methods. The data to be processed comprises talent personal information, recruitment company information, working content, skill requirements, working experience, salary and other information, and the comparison information is processed after the processing, so that the job applicant can automatically generate a job application form to be sent to the recruitment company according to the similarity of the comparison information;
the job entering analysis module 52 searches the recruiter information with accurate matching in the cloud according to the characteristic acquisition result and the evaluation result of the talent analysis module 51, analyzes the job entering condition required by the matched recruiter information, performs job entering judgment by combining the analysis result with the matched information related to the job seeker, and sends a job entering application to the recruiter or secondary matching corresponding recruiter information according to the judgment result;
collecting job seeker information including personal resume, past working experience, educational background, skill specialty, expectations for professional development and the like, then classifying and extracting key indexes, and establishing talent images;
mining position information meeting personal conditions and expectations of job seekers from resources such as recruitment websites, social media, recruitment release information and the like based on talent portrait, and performing unambiguous description and explanation on the positions;
for each position, evaluating the matching degree of the position according to the talent image of job seekers, comparing and sequencing the position with other positions, and preferentially recommending the position with the highest matching degree;
according to the selection of job seekers, automatically processing the contents of the job application, such as extracting personal information, editing job seekers, active interview preparation suggestions and the like, and converting the job seekers into job seekers sent to enterprise recruitment departments;
the method comprises the following steps:
establishing talent portraits of job seekers: describing and establishing a job seeker portrait by converting the background and the expectations of the job seeker into key indexes, namely determining the weight and the calculation method of each key index in the job seeker portrait;
building a position label: and carrying out natural language processing on the position description keywords in the transmission network and recruitment release information, matching the position labels with the knowledge base, and automatically grouping. Matching job seekers with job positions: determining the matching degree between the job seeker and the job position according to the talent portrait and the job position label of the job seeker by means of artificial intelligence and big data technology;
setting up a platform template and sending mails: and according to the matching degree of the job seeker and the job positions, establishing a Contact List for the job seeker and the relevant job positions, and sending a mail for each job position matching. Thereby realizing the fast and accurate matching of job seekers and enterprises.
The cloud storage unit 60 includes an analysis learning module 61 and a data updating module 62;
the analysis learning module 61 collects the job-seeking failure process of the job-seeking analysis module 52, evaluates the job-seeking failure process, and extracts the failure information caused by fine mismatch therefrom for analysis, with the following formula:
collecting reasons of job entering failure: relevant data for job entry failure is collected, including personal background information, interview feedback, performance, and the like.
Extracting key information: critical information and data of job entry failures, such as job matching, job tasks and professional goals, etc., are analyzed to help determine aspects to be improved and optimized.
Establishing a key index model: all information is integrated and analyzed to determine the key indicators and metrics of most interest and neutral assessment.
Artificial intelligence analysis: the most important questions and influencing factors are determined by big data analysis and artificial intelligence algorithms, and working adjustments are made according to these factors to achieve the best match.
Reselection and route position information: and updating the position information and restarting recruitment according to the analysis result, and practically collecting feedback of the recruiter, and continuously adjusting and optimizing the recruitment scheme. The measures need a belief of firm balance, continuously optimize job seekers and job information, analyze data by means of big data technology, continuously optimize and personalize the matching degree between the job seekers and the personnel units, and therefore realize benign interaction of professional development and enterprise development;
the data updating module 62 is configured to upload the analysis result of the data updating module 62 to the cloud, and update information for the cloud to perform job entering judgment for the job seeker matching position by the subsequent job entering analysis module 52.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the above-described embodiments, and that the above-described embodiments and descriptions are only preferred embodiments of the present invention, and are not intended to limit the invention, and that various changes and modifications may be made therein without departing from the spirit and scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (6)
1. The personnel data distribution system for correspondingly issuing recruitment information according to the delivery area is characterized in that: the system comprises an information collection unit (1), an information classification unit (2), a talent recommendation unit (50) and a cloud storage unit (60);
the information collecting unit (1) sends information to the job seeker information and recruiter information to fill in a collecting table, checks and verifies the collected job seeker information and recruiter information, and judges authenticity of the job seeker and recruiter according to a verification result;
the information classification unit (2) is used for analyzing according to the user information registered by the information collection unit (1), acquiring the demand information of job seekers and recruiters according to analysis data, and providing an information display channel according to the demand information similarity;
the talent recommending unit (50) is used for collecting the information of the job seeker in the information classifying unit (2), analyzing the information of the job seeker, obtaining high-end talent data, inquiring and evaluating the adapted recruiter information by utilizing a matching algorithm according to the high-end talent data, and sending a job seeker application according to an evaluation result;
the cloud storage unit (60) is used for analyzing and uploading the result of the job application sent by the talent recommendation unit (50), updating by combining the existing job application process, and updating a sending route for the talent recommendation unit (50) according to the latest cloud data;
the information collection unit (1) comprises an information registration module (10) and an information analysis module (20);
the information registration module (10) establishes a cloud end, and transmits the requirement intention filled in by the job seeker and the recruiter into the cloud end;
the information analysis module (20) is used for auditing the information collected by the cloud and removing invalid false information;
the information registration module (10) comprises a personal information collection module (11) and an enterprise information collection module (12);
the personal information collection module (11) provides information filling forms for job seekers so as to collect relevant information of the job seekers and send the relevant information to the cloud;
the enterprise information collection module (12) provides an information filling form for recruiters, so that information related to the recruiters is collected and sent to the cloud;
the information analysis module (20) comprises a personal analysis module (21) and an enterprise analysis module (22);
the personal analysis module (21) sends relevant information of job seekers in the cloud to the personal information collection module (11), judges false information of the information, and then reserves the information in the cloud according to the judging result;
the personal analysis module (21) sends the recruiter related information of the cloud end to the personal information collection module (11), judges false information of the information, and then reserves the information in the cloud end according to the judging result;
the talent recommendation unit (50) comprises a talent analysis module (51) and an job entry analysis module (52);
the talent analysis module (51) extracts job seekers with high evaluation coefficients from the evaluation results of the personal demand module (41), and performs characteristic collection and evaluation on the job seekers;
the job entering analysis module (52) searches accurate recruiter information in the cloud according to the characteristic acquisition result and the evaluation result of the talent analysis module (51), analyzes the job entering situation of the positions required by the matched recruiter information, performs job entering judgment by combining the analysis result with the matched related information of the job seeker, and sends a job entering application to the recruiter or secondary matching corresponding recruiter information according to the judgment result.
2. The personnel data distribution system for corresponding distribution of recruitment information according to the delivery area of claim 1, wherein the matching algorithm specifically comprises:
extracting talent parameter characteristics according to high-end talent data, and forming talent parameter vectors according to the talent parameter characteristics;
extracting recruitment parameter features according to recruiter information, and forming recruitment parameter vectors according to the recruitment parameter features;
using Manhattan distance meansThe Manhattan distance between the talent parameter vector and the recruitment parameter vector is calculated, so that matching is realized; the Manhattan distance formula isD：
，
wherein ,Xis a vector of the talent parameters,Yand recruiting the parameter vector.
3. The system for distributing personal data for corresponding distribution of recruitment information according to claim 1, wherein: the information classification unit (2) comprises a data storage module (30) and a data analysis module (40);
the data storage module (30) is used for carrying out characteristic analysis on the job seeker information and the recruiter information and classifying the information according to analysis characteristic results;
the data analysis module (40) searches for a match in the cloud according to requirements of job seekers and recruiters, and provides information recommendation according to a matching result.
4. A system for distributing personal data for corresponding distribution of recruitment information according to a drop-in area as claimed in claim 3, wherein: the data storage module (30) comprises a personal classification module (31) and an enterprise classification module (32);
the personal classification module (31) collects and analyzes the position requirements of the job seeker, and performs step classification according to the analysis result and different position requirements of the job seeker;
the enterprise classification module (32) collects and analyzes talent requirements required by recruiters, and classifies the talent requirements according to analysis results.
5. The system for distributing personal data for corresponding distribution of recruitment information according to claim 4, wherein: the data analysis module (40) comprises a personal demand module (41) and an enterprise demand module (42);
the personal demand module (41) is used for evaluating by combining the related information of the job seeker collected by the personal information collection module (11) and the analysis result of the personal classification module (31), and searching similar recruiter information in the cloud according to the evaluation result for recommendation;
the enterprise demand module (42) is used for evaluating the recruiter related information collected by the enterprise information collection module (12) and the analysis result of the enterprise classification module (32), and similar job seeker information is searched in the cloud for recommendation according to the evaluation result.
6. The system for distributing personal data for corresponding distribution of recruitment information according to claim 1, wherein: the cloud storage unit (60) comprises an analysis learning module (61) and a data updating module (62);
the analysis learning module (61) collects the job-seeking failure process of the job-seeking analysis module (52), evaluates the job-seeking failure process, and extracts failure information caused by fine mismatch from the job-seeking failure process to analyze;
the data updating module (62) is used for uploading the analysis result of the data updating module (62) to the cloud, and updating information for the cloud to enable the follow-up job entering analysis module (52) to carry out job entering judgment for the job seeker matching positions.
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