Intelligent Talent Recommendation and Cultivation Platform and Method Based on Learning Map and Post Portrait

Abstract

The present invention provides an intelligent talent recommendation and cultivation platform and method based on a learning map and a job portrait. In the platform, a job portrait analysis module receives the input recruitment positions, conducts portrait analysis on the recruitment positions, and obtains job portraits of each recruitment position reflecting its job description and quality and ability requirements; a learning map analysis module plans the promotion path and outputs a learning map based on the promotion path; a team analysis module conducts big data analysis on the job skills, knowledge, learning courses, learning interests, development potential and behaviors of employees within the team to obtain the ability / potential information of each employee; a recommendation module recommends employees to the recruitment positions according to the ability / potential information of the employees and the job portraits of the recruitment positions. The platform utilizes information technology and big data analysis to realize the function of intelligent job matching and improve the accuracy of matching.

Description

Technical Field

[0001] The present invention belongs to the technical field of human resources, and particularly relates to an intelligent talent recommendation and cultivation platform and method based on a learning map and a job portrait. Background Art

[0002] For job talent loss, enterprise transformation, or an enterprise entering a new field, it is necessary to find suitable job talents, and key positions need to pay attention to reserving backup talents. Currently, the qualified talents required for enterprise positions are mainly obtained through external recruitment and internal selection. The reference bases for internal talent selection are usually: past performance, competency, and qualifications. Performance can be measured through assessment, and qualifications such as education background, work experience are also easy to judge. Only competency is relatively difficult to judge.

[0003] The traditional methods for judging competency in talent selection are mainly: investigation, examination, interview, and probation for judgment. These methods are complex to operate, lack big data support, and cost a large amount of cost and time. Examinations and interviews can be passed by cramming, investigations also have limitations, and probation requires time cost. For most positions, the cost of trial and error is not low, affecting work efficiency.

[0004] Moreover, most employers select candidates based on job requirements. Before selecting candidates, they will conduct job analysis, compile job descriptions, and list the competency requirements. However, when selecting candidates, they basically look at the graduation major, work background, and personal resume, and judge through internal recommendations and human resources investigations. Little analysis is done on the learning knowledge of candidates and their preferred technical knowledge, and of course, there is a lack of suitable tools.

[0005] For employers who are consciously cultivating backup talents, the common approach is: select a master to take on an apprentice for technical positions, and promote a deputy or assistant first for management positions. This cultivation method is effective, but there are also problems. First, the selection of deputies or assistants is generally not very strict, and the scope of subsequent cultivation and selection is small. Second, the selection of talents during promotion is limited and cannot be widely selected. Summary of the Invention

[0006] Aiming at the defects in the prior art, the present application provides an intelligent talent recommendation and cultivation platform and method based on a learning map and a job portrait, which uses information technology and big data analysis to realize the function of intelligent job matching and improve the accuracy of matching.

[0007] In a first aspect, an intelligent talent recommendation and cultivation platform based on a learning map and a job portrait includes:

[0008] A job portrait analysis module: used to receive the input recruitment positions, conduct portrait analysis on the recruitment positions, and obtain job portraits reflecting their job descriptions and competency requirements for each recruitment position;

[0009] Learning Map Analysis Module: used for planning promotion paths and outputting learning maps based on promotion paths;

[0010] Team Analysis Module: used for performing big data analysis on the job skills, knowledge, learning courses, learning interests, development potential, and behaviors of employees within a team to obtain the ability / potential information of each employee;

[0011] Recommendation Module: used for recommending employees to recruitment positions based on the ability / potential information of employees and the job portraits of recruitment positions.

[0012] Preferably, the job portrait analysis module is specifically used for:

[0013] Dividing different recruitment positions into different job roles;

[0014] Defining corresponding job contents and responsibilities for each job role;

[0015] Defining corresponding skill knowledge for each job content to obtain the job portraits of the different recruitment positions.

[0016] Preferably, the learning map analysis module is specifically used for:

[0017] Obtaining the job portraits of the recruitment positions, matching corresponding courses and trainings for the skill knowledge in the job portraits, setting the job levels of the recruitment positions, and obtaining the promotion order based on the job levels of each recruitment position to obtain the promotion paths;

[0018] And / or setting the job levels of the recruitment positions, constructing a competency model for each job level, obtaining the competency qualities of the recruitment positions, and matching knowledge and courses for each competency quality to obtain the promotion paths.

[0019] Preferably, the team analysis module specifically includes:

[0020] Receiving the input courses or trainings;

[0021] Obtaining the knowledge mastered by employees within the team after completing the courses or trainings;

[0022] Weighting the knowledge mastered by each employee to obtain the ability, skill knowledge, learning ability, experience value, development value, and internal influence of the member corresponding to the recruitment position, constituting the ability / potential information.

[0023] Preferably, the recommendation module is specifically used for:

[0024] Matching the job portraits of the recruitment positions and the ability / potential information of each member respectively to obtain the matching degree of each member;

[0025] Rank the matching degrees of each member;

[0026] Set the top n members in the ranking as candidates;

[0027] Push the job profile of the corresponding recruitment position to the corresponding candidates.

[0028] Preferably, the platform further includes:

[0029] Interactive platform module: used to upload video courses, work documents or knowledge question banks; also supports knowledge Q&A and course sharing.

[0030] Preferably, the platform further includes:

[0031] Reserve personnel training module: used to match according to the job profiles of each reserve position and the ability / potential information of the members, formulate career development plans and training plans for each member according to the matching results, and push the corresponding courses.

[0032] In a second aspect, an intelligent talent recommendation and training method based on a learning map and a job profile includes the following steps:

[0033] Receive the input recruitment positions, conduct portrait analysis on the recruitment positions, and obtain the job profiles of each recruitment position reflecting its job description and quality and ability requirements;

[0034] Plan the promotion path and output a learning map based on the promotion path;

[0035] Conduct big data analysis on the job skills, knowledge, learning courses, learning interests, development potential and behaviors of the employees within the team to obtain the ability / potential information of each employee;

[0036] Recommend employees to the recruitment positions according to the ability / potential information of the employees and the job profiles of the recruitment positions.

[0037] Preferably, before the big data analysis on the job skills, knowledge, learning courses and behaviors of the employees within the team, the method further includes:

[0038] Upload video courses, work documents or knowledge question banks; also supports knowledge Q&A and course sharing.

[0039] Preferably, after the employees are recommended to the recruitment positions, the method further includes:

[0040] Match according to the job profiles of each reserve position and the ability / potential information of the members, formulate career development plans and training plans for each member according to the matching results, and push the corresponding courses.

[0041] As can be seen from the above technical solutions, the intelligent talent recommendation and cultivation platform and method based on learning maps and job portraits provided by the present invention have the following advantages:

[0042] 1. For the technical process and management of job portrait analysis, it is required to conduct job work portrait analysis and knowledge requirement analysis, enabling the employer to very precisely depict the clear job content, responsibilities, capabilities, and knowledge requirements for job talents.

[0043] 2. Through job portrait analysis and knowledge and ability refinement, a learning map based on the job promotion path is formed, creating a promotion and learning path for cultivating talents starting from basic positions, consciously cultivating talents on a large scale, and building a learning-oriented enterprise.

[0044] 3. Through big data analysis of members' learning, interaction, and sharing, weighted values are designed by referring to the memory curve to obtain the long-term and short-term knowledge and abilities of big data intelligent analysis. Algorithms are designed by combining the work background and the ability references of superior and subordinate positions, and the needs of past and future positions are considered. Through precise big data matching, talent recommendation and conscious cultivation for positions are realized, achieving the long-term development of the enterprise.

[0045] 4. The weighted algorithm can be optimized according to work changes and environmental assessment feedback, becoming more and more precise and more in line with the enterprise's needs.

[0046] 5. Realize job portrait analysis, build a learning-oriented enterprise, strengthen corporate culture construction, strengthen team interaction, and conduct big data analysis of overall and individual qualities. It enables the enterprise to base on the present and look forward to the future, providing a decision-making basis for the enterprise's team building, enterprise development, innovation, and exploration of new fields.

[0047] 6. Enable the enterprise to precisely cultivate and select talents, which can significantly reduce training costs, reduce the risk of job talent selection and trial-and-error costs, and provide an efficient platform for realizing the enterprise's strategy and the career development of enterprise members. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to actual scale.

[0049] Figure 1 It is the block diagram of the platform provided in Embodiment 1 of the present invention.

[0050] Figure 2 It is the processing flow chart of the job portrait analysis module provided in Embodiment 1 of the present invention.

[0051] Figure 3This is the processing flowchart of the learning map analysis module provided in the first embodiment of the present invention.

[0052] Figure 4 This is the processing flowchart of the team analysis module provided in the first embodiment of the present invention.

[0053] Figure 5 This is the processing flowchart of the recommendation module provided in the first embodiment of the present invention.

[0054] Figure 6 This is the processing flowchart of the interactive platform module provided in the first embodiment of the present invention.

[0055] Figure 7 This is the processing flowchart of the backup personnel training module provided in the first embodiment of the present invention. Detailed implementation manners

[0056] Hereinafter, embodiments of the technical solution of the present invention will be described in detail with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and thus are only examples and cannot be used to limit the protection scope of the present invention. It should be noted that unless otherwise specified, the technical terms or scientific terms used in this application should have the ordinary meanings understood by those skilled in the art to which the present invention belongs.

[0057] It should be understood that when used in this specification and the appended claims, the terms "comprises" and "comprising" indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0058] It should also be understood that the terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in this specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0059] As used in this specification and the appended claims, the term "if" can be interpreted as "when", "once", "in response to determining", or "in response to detecting" according to the context. Similarly, the phrase "if determined" or "if detecting [the described condition or event]" can be interpreted as meaning "once determined", "in response to determining", "once detecting [the described condition or event]", or "in response to detecting [the described condition or event]" according to the context.

[0060] Embodiment 1:

[0061] An intelligent talent recommendation and training platform based on a learning map and a job portrait, seeFigure 1 , including:

[0062] Job profile analysis module: used to receive the entered recruitment positions, perform profile analysis on the recruitment positions, and obtain the job profiles of each recruitment position; specifically used for:

[0063] Dividing different recruitment positions into different job roles; defining corresponding job contents and responsibilities for each job role; defining corresponding skill knowledge for each job content, so as to obtain the job profiles reflecting the job descriptions and quality and ability requirements of the different recruitment positions.

[0064] Specifically, for example, for the designer position, the job roles can be divided into UI designers and graphic designers. Among them, the main job content of graphic designers is divided into advertising design and graphic processing; and for each job content such as "graphic processing", corresponding skills are added, and corresponding skill courses are associated according to different skills. The job profile analysis module needs to classify and grade the positions, and perform job profile analysis on each level of positions. See Figure 2 , breaking down a job responsibility into different job roles, then defining corresponding job contents and responsibilities for each job role. And defining the required skill knowledge for each job content.

[0065] Learning map analysis module: used to plan the promotion path and output the learning map based on the promotion path; specifically used for:

[0066] Obtaining the job profiles of the recruitment positions, matching corresponding courses and trainings for the skill knowledge in the job profiles, setting the job levels of the recruitment positions, and obtaining the promotion order according to the job levels of each recruitment position, so as to obtain the promotion path;

[0067] And / or setting the job levels of the recruitment positions, constructing a competency model for each job level, obtaining the competency qualities of the recruitment positions, and matching knowledge and courses for each competency quality, so as to obtain the promotion path.

[0068] Specifically, the learning map analysis module is used to plan the job promotion path and output the learning map based on the promotion path. See Figure 3 , there are the following two ways to classify and grade the positions:

[0069] 1) Horizontal classification. Sort out the job contents, responsibilities and the skill knowledge required to complete the job contents from the job profiles, and match corresponding courses and trainings for the skill knowledge. Set the promotion order of the positions according to the job levels. For example, set it as a five-level promotion path. After the promotion path is set, the learning map is automatically output, and the learning map is applied to the enterprise individuals or teams.

[0070] 2) Vertical grading. This module can vertically divide positions into several levels: for example, Level 1, Level 2, and Level 3. The levels increase from low to high as the promotion path. This module constructs a competency model for each level of position, synthesizes the competency qualities of internal positions in the enterprise into a competency dictionary, matches knowledge and courses for the competency qualities, automatically outputs a learning map, and applies the learning map to individuals or teams in the enterprise.

[0071] Team analysis module: used to conduct big data analysis on the job skills, knowledge, learning courses, learning interests, development potential, and behaviors of employees within the team to obtain the ability / potential information of each employee; specifically used for:

[0072] Receive the entered courses or training; obtain the knowledge mastered by employees within the team after completing the courses or training; weight the knowledge mastered by each employee to obtain the ability, skill knowledge, learning ability, experience value, development value, and internal influence corresponding to the recruitment position of this member, constituting the said ability / potential information.

[0073] Specifically, the team analysis module mainly conducts big data analysis on the job skills, knowledge, learning courses, and sharing behaviors of team members. See Figure 4 , the employer conducts corresponding courses and training according to the enterprise strategy and the personal promotion and development of members (i.e., the learning map). While supporting the uploading of courses and documents, the attributes of the courses and documents are also set. Through the attributes, it can be seen which type of skills the position belongs to and which positions are suitable for individuals. The ability of employees is improved through online and offline training, and the knowledge learned by each employee is recorded through big data. At the same time, corresponding weighting can be carried out through the learning of knowledge associated with the learning map by employees, the self-selected learning of knowledge learned within the memory curve period, and classified knowledge courses. Big data analysis extracts the ability, skill knowledge, learning ability, experience value, development value, internal influence, etc. corresponding to the position of this member.

[0074] Recommendation module: used to recommend employees to the recruitment position according to the ability / potential information of employees and the job portrait of the recruitment position. Specifically used for:

[0075] Match the job portrait of the said recruitment position and the ability / potential information of each member to obtain the matching degree of each member; rank the matching degree of each member; set the top n members with the highest ranking as candidates; push the job portrait of the corresponding recruitment position to the corresponding candidates.

[0076] Specifically, the recommendation module is used for intelligent recommendation based on big data analysis. See Figure 5, matching the job profile with the individual's ability / potential information, job profile analysis to identify job responsibilities and skills and knowledge, ability / potential information analysis to refine the member's ability for the corresponding job, skills and knowledge, learning ability, experience, development value, internal influence, etc., and ranking and recommending employees according to the matching degree. After reserving candidates, this module also invites candidates to run for election through questionnaires and 360-degree environmental assessments, and can also recommend job learning content to job reserve personnel so that reserve personnel can achieve job competency.

[0077] Interactive platform module: used to upload video courses, work documents or knowledge question banks; also supports knowledge Q&A and course sharing.

[0078] Specifically, the interactive platform module is used to build a knowledge management and learning interactive platform to create a learning enterprise. Figure 6 The interactive platform module uploads video courses, work documents, knowledge question banks, etc. to the platform, and defines attributes for these contents (what kind of knowledge they belong to, what work capabilities they can improve). Functions such as knowledge questions and answers and micro-class sharing are enabled to import corresponding employees for different positions to achieve person-job matching. A learning map is formed through the promotion path to enable employees to clarify their promotion and learning directions. Enterprises deploy training and learning plans based on strategic goals and personnel growth. During the learning process, employees can obtain employee capability / potential information through big data to analyze and record their capabilities.

[0079] Reserve personnel training module: used to match the job profiles of each reserve position with the ability / potential information of the members, formulate career development plans and training plans for each member based on the matching results, and push corresponding courses.

[0080] Specifically, the reserve personnel training module is used to consciously train reserve talents for key internal positions. Figure 7 , pre-select personnel through job profiles and employees' ability / potential information, formulate career development plans and corresponding training plans for these personnel, and recommend compulsory and elective courses according to the talent training plan.

[0081] Based on the current problems in selecting talents within enterprises, the platform builds a big data platform based on enterprise development training, internal learning, culture and interaction. The platform allows enterprises to analyze and profile internal positions, that is, the job roles and work content of the personnel in the positions, and the skills and knowledge required to be competent for the job, so as to sort out what abilities and knowledge are required for the position. Through big data analysis of personnel's learning and knowledge, combined with content analysis and behavior analysis of long-term interactive sharing, talents who are suitable for the position in terms of knowledge level and learning ability are recommended. Then, through research, interviews and other methods, qualified talents for the position can be selected more accurately. The platform has the following advantages:

[0082] 1. The technical process and management of job profile analysis require job work profile analysis and knowledge requirement analysis, enabling employers to precisely depict the specific job content, responsibilities, capabilities, and knowledge requirements for job talents.

[0083] 2. Through job profile analysis and knowledge and ability refinement, a learning map based on the job promotion path is formed, creating a promotion and learning path for cultivating talents starting from basic positions, consciously cultivating talents on a large scale, and building a learning-oriented enterprise.

[0084] 3. Through big data analysis of members' learning, interaction, and sharing, weighted values are designed by referring to the forgetting curve to obtain the long-term and short-term knowledge and abilities through big data intelligent analysis. Algorithms are designed by combining the work background and the ability references of superior and subordinate positions, and considering the needs of past and future positions. Through precise big data matching, talent recommendation and conscious cultivation for positions are achieved, realizing the long-term development of the enterprise.

[0085] 4. The weighted algorithm can be optimized according to work changes and environmental assessment feedback, becoming more accurate with use and increasingly meeting the enterprise's needs.

[0086] 5. Implement job profile analysis, build a learning-oriented enterprise, strengthen corporate culture construction, enhance team interaction, and conduct big data analysis of overall and individual qualities. This enables the enterprise to base itself on the present and look to the future, providing a decision-making basis for the enterprise's team building, development, innovation, and exploration of new fields.

[0087] 6. Enable the enterprise to precisely cultivate and select talents, significantly reducing training costs, reducing the risks of job talent selection and trial-and-error costs, and providing an efficient platform for realizing the enterprise's strategy and the career development of enterprise members.

[0088] Example Two:

[0089] An intelligent talent recommendation and cultivation method based on a learning map and job profile, comprising the following steps:

[0090] Receive the input recruitment positions, conduct profile analysis on the recruitment positions to obtain the job profiles of each recruitment position;

[0091] Plan the promotion path and output a learning map based on the promotion path;

[0092] Conduct big data analysis on the job skills, knowledge, learning courses, and behaviors of employees within the team to obtain the ability / potential information of each employee;

[0093] Recommend employees to the recruitment positions according to the ability / potential information of the employees and the job profiles of the recruitment positions.

[0094] Preferably, before performing big data analysis on the job skills, knowledge, learning courses, and behaviors of employees within the team, the method further includes:

[0095] Upload video courses, work documents, or knowledge question banks; also support knowledge Q&A and course sharing.

[0096] Preferably, after recommending employees to the recruitment positions, the method further includes:

[0097] Match according to the job portraits of each backup position and the ability / potential information of the members, formulate career development plans and training plans for each member according to the matching results, and push the corresponding courses.

[0098] The method has the following advantages:

[0099] 1. The technical process and management of job portrait analysis require job work portrait analysis and knowledge requirement analysis, enabling the employer to very precisely depict the clear job content, responsibilities, abilities, and knowledge requirements for job talents.

[0100] 2. Through job portrait analysis and knowledge and ability refinement, a learning map based on the job promotion path is formed, creating a promotion and learning path for cultivating talents starting from basic positions, consciously cultivating talents on a large scale, and building a learning-oriented enterprise.

[0101] 3. Through big data analysis of members' learning, interaction, and sharing, design weighted values by referring to the memory curve to obtain the long-term and short-term knowledge and abilities of big data intelligent analysis. Combine the work background and the ability reference of superior and subordinate positions to design algorithms, and consider the needs of past and future positions. Through precise big data matching, realize the talent recommendation and conscious cultivation of positions, and achieve the long-term development of the enterprise.

[0102] 4. The weighted algorithm can be optimized according to work changes and environmental assessment feedback, becoming more and more precise and more in line with the enterprise's needs.

[0103] 5. Realize job portrait analysis, build a learning-oriented enterprise, strengthen corporate culture construction, strengthen team interaction, and conduct overall and individual quality big data analysis. Enable the enterprise to base on the present and look forward to the future, providing a decision-making basis for the enterprise's team building, enterprise development, innovation, and exploration of new fields.

[0104] 6. Enable the enterprise to precisely cultivate and select talents, significantly reduce training costs, reduce the risk of job talent selection and trial-and-error costs, and provide an efficient method for realizing the enterprise strategy and the career development of enterprise members.

[0105] For the method provided by the embodiments of the present invention, for the sake of brief description, for the parts not mentioned in the embodiment part, reference may be made to the corresponding content in the foregoing embodiments.

[0106] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the various embodiments of the present invention, and they should all be covered within the scope of the claims and the specification of the present invention.

Claims

1. An intelligent talent recommendation and cultivation platform based on a learning map and a job portrait, characterized in that, Including: Job Profile Analysis Module: It is used to receive the input recruitment positions, conduct profile analysis on the recruitment positions, and obtain job profiles that reflect their job descriptions and quality and ability requirements for each recruitment position; Specifically, the Job Profile Analysis Module is used for: Dividing different recruitment positions into different job roles; Defining corresponding job contents and responsibilities for each job role; Defining corresponding skill knowledge for each job content to obtain the job profiles of the different recruitment positions; Learning Map Analysis Module: It is used to plan the promotion path and output a learning map based on the promotion path. Specifically, it is used for: Obtaining the job profiles of the recruitment positions, matching corresponding courses and trainings for the skill knowledge in the job profiles, setting the job levels of the recruitment positions, and obtaining the promotion order based on the job levels of each recruitment position to obtain the promotion path; And / or setting the job levels of the recruitment positions, constructing a competency model for each job level, obtaining the competency qualities of the recruitment positions, and matching knowledge and courses for each competency quality to obtain the promotion path; Team Analysis Module: It is used to conduct big data analysis on the job skills, knowledge, learning courses, learning interests, development potential and behaviors of employees within the team to obtain the ability / potential information of each employee. Specifically, it is used for: Receiving the input courses or trainings; Obtaining the knowledge mastered by the employees within the team after completing the courses or trainings; Weighting the knowledge mastered by each employee to obtain the ability, possessed skill knowledge, learning ability, experience value, development value and internal influence of the employee corresponding to the recruitment position, which constitute the ability / potential information; Recommendation Module: It is used to recommend employees to the recruitment positions according to the ability / potential information of the employees and the job profiles of the recruitment positions. Specifically, it is used for: Matching the job profiles of the recruitment positions and the ability / potential information of each employee respectively to obtain the matching degree of each employee; Ranking the matching degree of each employee; Setting the top n employees in the ranking as candidates; Pushing the job profiles of the corresponding recruitment positions to the corresponding candidates; The Recommendation Module is used for intelligent recommendation based on big data analysis, matching the job profiles and the personal ability / potential information. The job profiles analyze the job responsibilities and skill knowledge, and the ability / potential information analyzes and extracts the ability, possessed skill knowledge, learning ability, experience value, development value and internal influence of the employee corresponding to the position, and ranks and recommends employees according to the matching degree. After determining the predetermined candidates, the Recommendation Module also invites the candidates to run for election through questionnaires and 360-degree environmental assessments, and recommends the job learning content to the job backup personnel to facilitate the backup personnel to achieve job competency; The platform further includes: Interactive Platform Module: It is used to upload video courses, work documents or knowledge question banks; it also supports knowledge Q&A and course sharing; The platform further includes: Backup Personnel Training Module: It is used to match the job profiles of each backup position and the ability / potential information of the employees, formulate career development plans and training plans for each employee according to the matching results, and push the corresponding courses.

2. An intelligent talent recommendation and cultivation method based on a learning map and a job portrait, characterized in that, Including the following steps: Receive the input recruitment positions, conduct a portrait analysis on the recruitment positions, and obtain the job portraits of each recruitment position that reflect its job description and quality and ability requirements; specifically include: Divide different recruitment positions into different job roles; Define the corresponding work content and responsibilities for each job role; Define corresponding skills and knowledge for each job content to obtain job profiles for the different recruitment positions; Plan the promotion path and output a learning map based on the promotion path, including: Obtaining the job profile of the recruitment position, matching corresponding courses and training according to the skills and knowledge in the job profile, setting the job level of the recruitment position, and obtaining the promotion order according to the job level of each recruitment position to obtain the promotion path; and / or setting job levels for recruitment positions, constructing competency models for each job level, obtaining competency qualities for recruitment positions, matching knowledge and courses for each competency quality, so as to obtain the promotion path; Conduct big data analysis on the job skills, knowledge, learning courses, learning interests, development potential and behavior of team members to obtain the ability / potential information of each employee, including: Receive admission to courses or training; Acquire the knowledge acquired by team members after completing courses or training; Weight the knowledge possessed by each employee to obtain the employee's ability, skills and knowledge, learning ability, experience value, development value and internal influence corresponding to the recruitment position, which constitutes the ability / potential information; Recommend employees to recruitment positions based on their ability / potential information and the job profile of the recruitment position, including: Matching the job profiles of the recruitment positions with the ability / potential information of each employee to obtain the matching degree of each employee; Rank each employee’s match; Set the top n employees as candidates; Push the job profile of the corresponding recruitment position to the corresponding candidate; Specifically, intelligent recommendations are made based on big data analysis, matching job profiles with individual ability / potential information. Job profiles analyze job responsibilities and skills and knowledge, and ability / potential information analysis extracts the employee's ability for the corresponding job, skills and knowledge, learning ability, experience, development value, and internal influence. Employees are ranked and recommended based on the degree of matching. After the candidates are reserved, candidates are invited to run for election through questionnaires and 360-degree environmental assessments. Job learning content is also recommended to reserve candidates so that they can achieve job competency. Before the method performs big data analysis on the job skills, knowledge, learning courses and behaviors of the employees in the team, it also includes: Upload video courses, work documents or knowledge question banks; also supports knowledge Q&A and course sharing; After recommending employees to recruitment positions, the method further includes: Matching is performed based on the job profiles of each reserve position and the ability / potential information of the employees. Based on the matching results, career development plans and training plans are formulated for each employee, and corresponding courses are pushed.

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