Dynamic resume optimization method and system based on natural language processing and intelligent analysis

Abstract

The invention is suitable for the technical field of resume optimization, and provides a dynamic resume optimization method and system based on natural language processing and intelligent analysis, and the method comprises the following steps: receiving resume information, a target industry and a target position inputted by a user; resume information is automatically extracted and analyzed based on the natural language processing technology, key information and potential problems are obtained, the key information comprises work experience, education background and skill certificates, and the potential problems comprise the lack of necessary information, format inconsistency and content redundancy; determining demand information of the target position, matching the key information with the demand information, and generating an optimization suggestion according to a matching result; and selecting a resume template from the template library according to the target industry and the target position, automatically filling the resume information into the resume template, and adjusting the layout and format of the content of the resume information. According to the method, the efficiency and accuracy of resume optimization can be improved, manual intervention is reduced, and a more personalized and real-time resume improvement scheme is provided.

Description

Technical Field

[0001] The present invention relates to the technical field of resume optimization, and in particular to a dynamic resume optimization method and system based on natural language processing and intelligent analysis. Background Art

[0002] In today's workplace, resumes are the core tool for job seekers to demonstrate their abilities and experience. The traditional resume optimization process usually relies on manual editing, which is not only time-consuming and labor-intensive, but may also lead to different results due to differences in personal experience and subjective judgment. Although there are some resume optimization tools on the market, most of these tools can only provide limited automation functions and often fail to meet the needs of personalization and comprehensiveness.

[0003] However, job seekers usually need to rely on personal experience or seek help from professionals to optimize their resumes, a process that is time-consuming and may incur additional costs. Some automated tools currently on the market can provide basic format suggestions and templates, but they lack support for in-depth analysis of resume content and personalized optimization suggestions, and cannot meet diverse and complex needs. Traditional optimization methods are easily limited by the subjective judgment of editors and ignore the actual needs and industry standards of recruiters. Therefore, it is necessary to provide a dynamic resume optimization method and system based on natural language processing and intelligent analysis to solve the above problems. Summary of the invention

[0004] In view of the deficiencies in the prior art, the purpose of the present invention is to provide a dynamic resume optimization method and system based on natural language processing and intelligent analysis to solve the problems existing in the above-mentioned background technology.

[0005] The present invention is implemented as follows: a dynamic resume optimization method based on natural language processing and intelligent analysis, the method comprising the following steps:

[0006] Receive resume information, target industry and target position input by the user;

[0007] Preprocessing the resume information, automatically extracting and analyzing the resume information based on natural language processing technology to obtain key information and potential problems, the key information includes work experience, educational background and skill certificates, and the potential problems include lack of necessary information, inconsistent format and redundant content;

[0008] Determine the demand information of the target position, match the key information with the demand information, and generate optimization suggestions based on the matching results;

[0009] Select a resume template from the template library according to the target industry and target position, automatically fill the resume information into the resume template, and adjust the layout and format of the resume information content.

[0010] As a further solution of the present invention: the step of preprocessing the resume information specifically includes:

[0011] Performing text content analysis on the resume information, wherein the text content analysis includes plain text extraction and rich text extraction;

[0012] The resume information is formatted in a standardized manner, including date format unification, list and paragraph formatting.

[0013] As a further solution of the present invention: the steps of automatically extracting and analyzing resume information based on natural language processing technology to obtain key information and potential problems specifically include:

[0014] Perform word segmentation on the text content of the resume information, identify and mark key entities, including company name, position, skills, and work achievements;

[0015] Extract key information from the resume information, including work experience, education background, and skill certificates;

[0016] Problem detection is performed, including information missing detection and format error detection, and context analysis is performed to identify potential problems in the resume.

[0017] As a further solution of the present invention: the step of determining the requirement information of the target position, matching the key information with the requirement information, and generating optimization suggestions according to the matching results specifically includes:

[0018] Perform skill requirement analysis and experience requirement analysis based on the target position to determine the demand information of the target position, including essential skills, bonus skills, industry experience, and project management experience;

[0019] Match key information with demand information, and conduct skill matching and experience matching assessments;

[0020] Optimization suggestions are generated based on the matching results, and the optimization suggestions include skill improvement suggestions, experience supplement suggestions, format adjustment suggestions, and language optimization suggestions.

[0021] As a further solution of the present invention: the method also includes: collecting user feedback information and optimizing it, the user feedback channels include online questionnaires and email feedback; during the optimization, the resume template will be regularly updated, and the algorithm optimization iteration will be performed.

[0022] Another object of the present invention is to provide a dynamic resume optimization system based on natural language processing and intelligent analysis, the system comprising:

[0023] The resume information upload module is used to receive the resume information, target industry and target position input by the user;

[0024] A natural language processing module is used to pre-process the resume information, automatically extract and analyze the resume information based on natural language processing technology, and obtain key information and potential problems. The key information includes work experience, educational background and skill certificates, and the potential problems include lack of necessary information, inconsistent format and redundant content;

[0025] The optimization suggestion generation module is used to determine the demand information of the target position, match the key information with the demand information, and generate optimization suggestions based on the matching results;

[0026] The resume template filling module is used to select a resume template from a template library according to the target industry and target position, automatically fill the resume information into the resume template, and adjust the layout and format of the resume information content.

[0027] As a further solution of the present invention: the natural language processing module includes:

[0028] A text content parsing unit, used for performing text content parsing on the resume information, wherein the text content parsing includes plain text extraction and rich text extraction;

[0029] The format standardization unit is used to standardize the format of resume information, and the format standardization includes date format unification, list and paragraph formatting.

[0030] As a further solution of the present invention: the natural language processing module also includes:

[0031] The key entity tagging unit is used to perform word segmentation on the text content of the resume information, identify and tag key entities, including company name, position, skills and work achievements;

[0032] A key information extraction unit, used to extract key information from the resume information, the key information including work experience, educational background and skill certificates;

[0033] The potential problem identification unit is used to perform problem detection, including information missing detection and format error detection, and perform context analysis to identify potential problems in the resume.

[0034] As a further solution of the present invention: the optimization suggestion generating module includes:

[0035] A demand information determination unit, configured to perform skill requirement analysis and experience requirement analysis based on the target position, and determine the demand information of the target position, wherein the demand information includes essential skills, bonus skills, industry experience, and project management experience;

[0036] Demand information matching unit, used to match key information with demand information, and to perform skill matching assessment and experience matching assessment;

[0037] The optimization suggestion determination unit is used to generate optimization suggestions according to the matching results, wherein the optimization suggestions include skill improvement suggestions, experience supplement suggestions, format adjustment suggestions and language optimization suggestions.

[0038] As a further solution of the present invention: the system also includes an information feedback optimization module, which is used to collect user feedback information and optimize it. The user's feedback channels include online questionnaires and email feedback. During the optimization, the resume template will be regularly updated and the algorithm optimization iteration will be performed.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] The present invention uses natural language processing technology to automatically extract and analyze resume content, identify key information and potential problems, generate personalized optimization suggestions based on user resumes and job requirements, improve the matching degree and quality of resumes, and automatically generate resume templates that meet industry standards and user needs, simplifying the production process. The present invention can improve the efficiency and accuracy of resume optimization, reduce manual intervention, and provide a more personalized and real-time resume improvement plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is a flow chart of a dynamic resume optimization method based on natural language processing and intelligent analysis.

[0042] Figure 2 The present invention is a flowchart for preprocessing resume information in a dynamic resume optimization method based on natural language processing and intelligent analysis.

[0043] Figure 3 A flowchart for obtaining key information and potential problems in a dynamic resume optimization method based on natural language processing and intelligent analysis.

[0044] Figure 4 A flowchart for generating optimization suggestions based on matching results in a dynamic resume optimization method based on natural language processing and intelligent analysis.

[0045] Figure 5 This is a structural diagram of the dynamic resume optimization system based on natural language processing and intelligent analysis. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with the accompanying drawings and specific 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.

[0047] The specific implementation of the present invention is described in detail below in conjunction with specific embodiments.

[0048] like Figure 1 As shown, an embodiment of the present invention provides a dynamic resume optimization method based on natural language processing and intelligent analysis, and the method includes the following steps:

[0049] S100, receiving resume information, target industry and target position input by a user;

[0050] S200, preprocessing the resume information, automatically extracting and analyzing the resume information based on natural language processing technology, and obtaining key information and potential problems, wherein the key information includes work experience, educational background, and skill certificates, and the potential problems include lack of necessary information, inconsistent format, and redundant content;

[0051] S300, determining the demand information of the target position, matching the key information with the demand information, and generating optimization suggestions based on the matching results;

[0052] S400, selecting a resume template from a template library according to a target industry and a target position, automatically filling the resume information into the resume template, and adjusting the layout and format of the resume information content.

[0053] In the embodiment of the present invention, the user first needs to upload resume information (such as a PDF file or a Word document) and select the target industry and target position. Then the embodiment of the present invention will pre-process the resume information, including text extraction and format standardization, automatically extract and analyze the resume information based on natural language processing technology, obtain key information and potential problems, the key information includes work experience, educational background and skill certificates, and the potential problems include lack of necessary information, inconsistent format and redundant content, which is convenient for subsequent suggestions. Then the needs of the target position will be analyzed, the demand information of the target position will be determined, the key information will be matched with the demand information, the improvement space will be determined, and optimization suggestions will be generated based on the matching results. It can also combine industry standards and user background to provide personalized suggestions to enhance the competitiveness of the resume. In the embodiment of the present invention, a template library is established in advance, and the template library covers different industries and job types. A resume template is selected from the template library according to the target industry and target position, and the resume information is automatically filled into the resume template. The layout and format of the resume information content are adjusted, and finally a final resume version that meets the optimization suggestions is generated, which is usually provided for download in PDF format. In addition, the method also includes: collecting user feedback information and optimizing it, and the user feedback channels include online questionnaires and email feedback; when optimizing, the resume template will be updated regularly, and the algorithm optimization iteration will be performed to achieve better results.

[0054] like Figure 2 and Figure 3 As shown, as a preferred embodiment of the present invention, the step of preprocessing the resume information specifically includes:

[0055] S201, performing text content analysis on the resume information, wherein the text content analysis includes plain text extraction and rich text extraction;

[0056] S202, standardizing the format of the resume information, wherein the format standardization includes standardizing the date format, and formatting the lists and paragraphs.

[0057] In the embodiment of the present invention, the steps of automatically extracting and analyzing resume information based on natural language processing technology to obtain key information and potential problems specifically include:

[0058] S203, performing word segmentation processing on the text content of the resume information, identifying and marking key entities, which include company name, position, skills and work achievements;

[0059] S204, extracting key information from the resume information, the key information including work experience, educational background and skill certificates;

[0060] S205, performing problem detection, wherein the problem detection includes information missing detection and format error detection, and performing context analysis to identify potential problems in the resume.

[0061] In the embodiment of the present invention, the information missing detection includes missing key information and blank work experience, and the format error detection includes typesetting errors and grammatical errors.

[0062] like Figure 4 As shown, as a preferred embodiment of the present invention, the steps of determining the requirement information of the target position, matching the key information with the requirement information, and generating optimization suggestions according to the matching results specifically include:

[0063] S301, performing skill requirement analysis and experience requirement analysis based on the target position to determine the demand information of the target position, wherein the demand information includes essential skills, bonus skills, industry experience, and project management experience;

[0064] S302, matching the key information with the demand information, and performing skill matching evaluation and experience matching evaluation;

[0065] S303, generating optimization suggestions according to the matching results, wherein the optimization suggestions include skill improvement suggestions, experience supplement suggestions, format adjustment suggestions, and language optimization suggestions.

[0066] In the embodiment of the present invention, the experience matching evaluation includes relevance evaluation and depth evaluation, and finally obtains skill improvement suggestions, experience supplement suggestions, format adjustment suggestions and language optimization suggestions to facilitate users to modify and supplement their resumes.

[0067] like Figure 5 As shown, an embodiment of the present invention further provides a dynamic resume optimization system based on natural language processing and intelligent analysis, the system comprising:

[0068] The resume information uploading module 100 is used to receive the resume information, target industry and target position input by the user;

[0069] A natural language processing module 200 is used to pre-process the resume information, automatically extract and analyze the resume information based on natural language processing technology, and obtain key information and potential problems. The key information includes work experience, educational background and skill certificates, and the potential problems include lack of necessary information, inconsistent format and redundant content;

[0070] The optimization suggestion generation module 300 is used to determine the requirement information of the target position, match the key information with the requirement information, and generate optimization suggestions based on the matching results;

[0071] The resume template filling module 400 is used to select a resume template from a template library according to a target industry and a target position, automatically fill the resume information into the resume template, and adjust the layout and format of the resume information content.

[0072] As a preferred embodiment of the present invention, the natural language processing module 200 includes:

[0073] A text content parsing unit, used for performing text content parsing on the resume information, wherein the text content parsing includes plain text extraction and rich text extraction;

[0074] The format standardization unit is used to standardize the format of resume information, and the format standardization includes date format unification, list and paragraph formatting.

[0075] As a preferred embodiment of the present invention, the natural language processing module 200 further includes:

[0076] The key entity tagging unit is used to perform word segmentation on the text content of the resume information, identify and tag key entities, including company name, position, skills and work achievements;

[0077] A key information extraction unit, used to extract key information from the resume information, the key information including work experience, educational background and skill certificates;

[0078] The potential problem identification unit is used to perform problem detection, including information missing detection and format error detection, and perform context analysis to identify potential problems in the resume.

[0079] As a preferred embodiment of the present invention, the optimization suggestion generating module 300 includes:

[0080] A demand information determination unit, configured to perform skill requirement analysis and experience requirement analysis based on the target position, and determine the demand information of the target position, wherein the demand information includes essential skills, bonus skills, industry experience, and project management experience;

[0081] Demand information matching unit, used to match key information with demand information, and to perform skill matching assessment and experience matching assessment;

[0082] The optimization suggestion determination unit is used to generate optimization suggestions according to the matching results, wherein the optimization suggestions include skill improvement suggestions, experience supplement suggestions, format adjustment suggestions and language optimization suggestions.

[0083] As a preferred embodiment of the present invention, the system also includes an information feedback optimization module, which is used to collect user feedback information and optimize it. The user's feedback channels include online questionnaires and email feedback. During the optimization, the resume template will be regularly updated and the algorithm optimization iteration will be performed.

[0084] The embodiments of the present invention strictly abide by the laws and regulations of the country and region on data protection and privacy, formulate and publish a clear privacy policy, and clearly inform users of the collection, use, sharing and protection measures of their data. Before collecting user data, clearly inform users of the purpose of the data and obtain their explicit consent.

[0085] The above only describes in detail the preferred embodiments of the present invention, which is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

[0086] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0087] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0088] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the disclosure in the specification and examples. This application is intended to cover any variations, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the claims.

Claims

1. A dynamic resume optimization method based on natural language processing and intelligent analysis, characterized in that: The method comprises the following steps: Receive resume information, target industry and target position input by the user; Preprocessing the resume information, automatically extracting and analyzing the resume information based on natural language processing technology to obtain key information and potential problems, the key information includes work experience, educational background and skill certificates, and the potential problems include lack of necessary information, inconsistent format and redundant content; Determine the demand information of the target position, match the key information with the demand information, and generate optimization suggestions based on the matching results; Select a resume template from the template library according to the target industry and target position, automatically fill the resume information into the resume template, and adjust the layout and format of the resume information content.

2. The dynamic resume optimization method based on natural language processing and intelligent analysis according to claim 1 is characterized in that: The step of preprocessing the resume information specifically includes: Performing text content analysis on the resume information, wherein the text content analysis includes plain text extraction and rich text extraction; The resume information is formatted in a standardized manner, including date format unification, list and paragraph formatting.

3. The dynamic resume optimization method based on natural language processing and intelligent analysis according to claim 1 is characterized in that: The steps of automatically extracting and analyzing resume information based on natural language processing technology to obtain key information and potential problems include: Perform word segmentation on the text content of the resume information, identify and mark key entities, including company name, position, skills, and work achievements; Extract key information from the resume information, including work experience, education background, and skill certificates; Problem detection is performed, including information missing detection and format error detection, and context analysis is performed to identify potential problems in the resume.

4. The dynamic resume optimization method based on natural language processing and intelligent analysis according to claim 1 is characterized in that: The steps of determining the requirement information of the target position, matching the key information with the requirement information, and generating optimization suggestions according to the matching results specifically include: Perform skill requirement analysis and experience requirement analysis based on the target position to determine the demand information of the target position, including essential skills, bonus skills, industry experience, and project management experience; Match key information with demand information, and conduct skill matching and experience matching assessments; Optimization suggestions are generated based on the matching results, and the optimization suggestions include skill improvement suggestions, experience supplement suggestions, format adjustment suggestions, and language optimization suggestions.

5. The dynamic resume optimization method based on natural language processing and intelligent analysis according to claim 1 is characterized in that: The method also includes: collecting user feedback information and optimizing it, where the user feedback channels include online questionnaires and email feedback; during the optimization, the resume template will be regularly updated, and algorithm optimization iterations will be performed.

6. Dynamic resume optimization system based on natural language processing and intelligent analysis, characterized by: The system comprises: The resume information upload module is used to receive the resume information, target industry and target position input by the user; A natural language processing module is used to pre-process the resume information, automatically extract and analyze the resume information based on natural language processing technology, and obtain key information and potential problems. The key information includes work experience, educational background and skill certificates, and the potential problems include lack of necessary information, inconsistent format and redundant content; The optimization suggestion generation module is used to determine the demand information of the target position, match the key information with the demand information, and generate optimization suggestions based on the matching results; The resume template filling module is used to select a resume template from a template library according to the target industry and target position, automatically fill the resume information into the resume template, and adjust the layout and format of the resume information content.

7. The dynamic resume optimization system based on natural language processing and intelligent analysis according to claim 6 is characterized in that: The natural language processing module includes: A text content parsing unit, used for performing text content parsing on the resume information, wherein the text content parsing includes plain text extraction and rich text extraction; The format standardization unit is used to standardize the format of resume information, and the format standardization includes date format unification, list and paragraph formatting.

8. The dynamic resume optimization system based on natural language processing and intelligent analysis according to claim 6 is characterized in that: The natural language processing module also includes: The key entity tagging unit is used to perform word segmentation on the text content of the resume information, identify and tag key entities, including company name, position, skills and work achievements; A key information extraction unit, used to extract key information from the resume information, the key information including work experience, educational background and skill certificates; The potential problem identification unit is used to perform problem detection, including information missing detection and format error detection, and perform context analysis to identify potential problems in the resume.

9. The dynamic resume optimization system based on natural language processing and intelligent analysis according to claim 6 is characterized in that: The optimization suggestion generation module includes: A demand information determination unit, configured to perform skill requirement analysis and experience requirement analysis based on the target position, and determine the demand information of the target position, wherein the demand information includes essential skills, bonus skills, industry experience, and project management experience; Demand information matching unit, used to match key information with demand information, and to perform skill matching evaluation and experience matching evaluation; The optimization suggestion determination unit is used to generate optimization suggestions according to the matching results, wherein the optimization suggestions include skill improvement suggestions, experience supplement suggestions, format adjustment suggestions and language optimization suggestions.

10. The dynamic resume optimization system based on natural language processing and intelligent analysis according to claim 6 is characterized in that: The system also includes an information feedback optimization module, which is used to collect user feedback information and optimize it. The user's feedback channels include online questionnaires and email feedback. During the optimization, the resume template will be updated regularly and the algorithm optimization iteration will be performed.

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