Method, system and device for intelligently analyzing resume matching post and storage medium

Abstract

The invention provides a method for intelligently analyzing a resume matching post. The method comprises the following steps: analyzing resume text information through a text algorithm to obtain a job application label set; analyzing the position text information through a text algorithm to obtain a position tag set; comparing each job-hunting label in the job-hunting label set with each position label in the position label set, and comparing each job-hunting label and the number of labels matched with each position label; according to the number of the labels matched with the number of the job hunting labels and the number of the position labels, job hunters with the number lower than the number of the matched labels of a preset value are eliminated, and job hunters with the number higher than or equal to the number of the matched labels of the preset value are pre-recorded; and for the job seekers whose pre-admission is higher than or equal to a preset value of the number of the matching labels, comparing the matching proportion number of the job hunting labels of the job seekers and the emphasized position labels of the enterprise so as to judge whether the job seekers meet the final admission condition or not. Therefore, the matching accuracy and efficiency of the resumes and the posts are improved, and the HR manual screening and resume matching time of an enterprise is greatly shortened.

Description

Technical Field

[0001] The present invention relates to the technical field of job matching, and in particular to a method, system, device and storage medium for intelligently analyzing resumes and matching jobs. Background Art

[0002] With the rapid development of information technology and the intensified competition in the talent market, companies are faced with the problem of matching a large number of job seekers with job requirements during the recruitment process. Traditional recruitment methods often rely on manual screening of resumes, which is not only time-consuming but also easily affected by the subjective judgment of recruiters, resulting in the omission of suitable candidates or improper screening.

[0003] In modern enterprises, recruitment is an important part of human resource management. According to statistics, when recruiting new employees, enterprises receive dozens to hundreds of resumes for each position on average. Recruiters need to screen a large number of resumes within a limited time to find candidates who meet the job requirements. However, traditional manual screening methods are not only inefficient, but may also cause key content of resumes to be overlooked or misunderstood. Especially when facing high-skilled positions, it is difficult for recruiters to quickly identify candidates who meet specific skill requirements among many resumes.

[0004] Although some technologies have been successful in the resume matching field, there are still many challenges, including how to handle diverse resume formats and how to more accurately understand skill descriptions in different contexts. In addition, existing systems usually lack the ability to interact with users and cannot provide more in-depth comprehensive analysis and recommendations. Summary of the invention

[0005] The embodiments of the present disclosure provide a method, system, device and storage medium for intelligently analyzing resume matching to improve the accuracy of resume matching to positions.

[0006] In a first aspect, the present disclosure provides a method for intelligently analyzing resumes and matching positions, comprising the following steps:

[0007] Step S1, obtaining resume text information of a job seeker, and parsing the resume text information through a text algorithm to obtain a job search tag set;

[0008] Step S2, obtaining the position text information of the enterprise, and parsing the position text information through a text algorithm to obtain a position tag set;

[0009] Step S3, comparing each job search tag in the job search tag set with each position tag in the position tag set, and comparing the number of tags that match between each job search tag and each position tag;

[0010] Step S4, according to the number of tags that match each job-seeking tag and each position tag, eliminating job seekers whose number of tags that match is less than a preset value and pre-admitting job seekers whose number of tags that match is greater than or equal to a preset value;

[0011] Step S5: For job seekers who are pre-admitted with a number of matching tags greater than or equal to a preset value, the matching ratio between the job-seeking tags of the job seekers and the key position tags of the enterprises is compared to determine whether the job seekers meet the final admission conditions.

[0012] The step S1 is specifically as follows:

[0013] Creating a job seeker user interface, allowing job seekers to submit their resumes by uploading documents or directly entering text through the job seeker user interface;

[0014] Store the resumes uploaded by job seekers in the database to facilitate subsequent analysis of the resumes;

[0015] For the uploaded resume, use text extraction tools to convert the resume content into plain text format to obtain the resume text information;

[0016] The resume text information is parsed using a text algorithm to obtain a job search tag set.

[0017] The step S2 is specifically as follows:

[0018] Creating an enterprise user interface, allowing enterprises to upload recruitment information through the enterprise user interface;

[0019] Store the recruitment information uploaded by the enterprise in the database to facilitate subsequent analysis of the recruitment information;

[0020] For the uploaded recruitment information, use text extraction tools to convert the recruitment information into plain text format to obtain the position text information;

[0021] The position text information is parsed using a text algorithm to obtain a position label set.

[0022] The step S3 is specifically as follows:

[0023] Collect job seekers' job tag sets and companies' job tag sets;

[0024] Make sure the labels in both labels are in the same format to facilitate comparison;

[0025] Compare the elements in the two label sets and count the number of overlapping labels to obtain the number of matching labels for the job seekers.

[0026] The step S4 is specifically as follows:

[0027] Pre-set the threshold for screening the number of matching labels;

[0028] Compare the number of matching labels of the job seeker with a preset matching label number threshold;

[0029] If the number of matching labels of a job seeker is lower than the preset matching label number threshold, the job seeker will be eliminated;

[0030] If the number of matching labels of a job seeker is higher than or equal to the preset matching label number threshold, the job seeker is pre-admitted.

[0031] The step S5 is specifically as follows:

[0032] Determine which job tags are important in the company's job tag collection to obtain the company's focused job tags;

[0033] Calculate the number of matches between each job search tag in the job search tag set of the pre-admitted job seekers and each of the company's focus position tags;

[0034] The matching ratio is obtained by dividing the number of matches by the total number of job tags of the job seeker;

[0035] The matching ratio is compared with the preset qualification standard to determine whether the job applicant meets the final admission requirements.

[0036] In a second aspect, the present disclosure also provides a resume matching job system, including:

[0037] Client module, used for job seekers to upload resumes and companies to upload recruitment information;

[0038] Data storage module, used to store job seekers' resumes and companies' recruitment information;

[0039] A text extraction module is used to extract resume text information from job seekers' resumes and position text information from companies' recruitment information;

[0040] The parsing module is configured with a large language model and a Sentence-BERT model, which is used to parse the resume text information and the position text information to obtain the job application tag set and the position tag set;

[0041] The tag number comparison module is used to compare and calculate the number of matching tags between each job tag in the job tag set and each position tag in the position tag set, and to eliminate or pre-admit a job seeker based on the number of matching tags;

[0042] The admission calculation module is used to calculate the matching ratio between each job search tag in the job search tag set of the pre-admitted job seekers and each key position tag of the enterprise, so as to determine whether the job seeker meets the final admission conditions.

[0043] The client module also includes:

[0044] The first user interface is used by job seekers to upload resumes;

[0045] The second user interface is used by enterprises to upload recruitment information;

[0046] Ai dialogue interface, used for enterprise HR to conduct artificial intelligence dialogue.

[0047] In a third aspect, an embodiment of the present disclosure further provides an electronic device, including:

[0048] one or more processing devices;

[0049] A storage device for storing one or more programs;

[0050] When the one or more programs are executed by the one or more processing devices, the one or more processing devices implement the intelligent resume analysis and job matching method.

[0051] In a fourth aspect, an embodiment of the present disclosure further provides a computer-readable storage medium storing a computer program, which, when executed by one or more processing devices, implements the method for intelligently analyzing resumes and matching positions.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] In the present invention, by obtaining the resume text information of the job seeker, the resume text information is parsed by a text algorithm to obtain a job search tag set; the position text information of the enterprise is obtained, and the position text information is parsed by a text algorithm to obtain a position tag set; each job search tag in the job search tag set is compared with each position tag in the position tag set, and the number of tags that match each job search tag and each position tag is compared; according to the number of tags that match each job search tag and each position tag, the job seekers with the number of matching tags less than the preset value and the job seekers with the number of matching tags higher than or equal to the preset value are eliminated; for the job seekers with the number of matching tags higher than or equal to the preset value, the matching proportion of the job search tag of the job seeker and the company's focus position tag is compared to determine whether the job seeker meets the final admission conditions. Thereby, the matching accuracy and efficiency of resumes and positions can be improved, and a large number of job seekers' resumes and recruitment information can be automatically analyzed and processed, thereby greatly reducing the time for enterprise HR to manually screen and match resumes, and improving the efficiency of the overall recruitment process. And it can reduce labor costs, reduce the workload of enterprise HR in the recruitment process, so that enterprise HR can devote more time and energy to more strategic work. Efficient and accurate recruitment methods can help companies quickly find suitable job seekers for the job positions, shorten the recruitment cycle, and enhance the company's talent acquisition capabilities and overall competitiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] One or more embodiments are exemplarily described by pictures in the corresponding drawings, and these exemplified descriptions do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings represent similar elements, and unless otherwise stated, the figures in the drawings do not constitute proportional limitations.

[0055] Figure 1 A flow chart of the method for intelligently analyzing resumes and matching positions of the present invention;

[0056] Figure 2 This is a module diagram of the intelligent resume analysis and job matching system of the present invention;

[0057] Figure 3 It is a module diagram of the client module of the present invention.

[0058] Description of reference numerals:

[0059] 10. Client module; 101. First user interface; 102. Second user interface; 103. Ai dialogue interface; 20. Data storage module; 30. Text extraction module; 40. Parsing module; 50. Label number comparison module; 60. Admission calculation module. DETAILED DESCRIPTION

[0060] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0061] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0062] The term "including" and its variations used herein are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0063] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0064]

Ordinal Numbers

[0065] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0066] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0067] Embodiment 1,

[0068] Figure 1 The flowchart of the method for intelligently analyzing resumes and matching positions of the present invention is as follows. The present disclosure provides a method for intelligently analyzing resumes and matching positions, comprising the following steps:

[0069] Step S1, obtaining resume text information of a job seeker, and parsing the resume text information through a text algorithm to obtain a job search tag set;

[0070] The step S1 is specifically as follows:

[0071] Creating a job seeker user interface, allowing job seekers to submit their resumes by uploading documents or directly entering text through the job seeker user interface;

[0072] Store the resumes uploaded by job seekers in the database to facilitate subsequent analysis of the resumes;

[0073] For the uploaded resume, use text extraction tools to convert the resume content into plain text format to obtain the resume text information;

[0074] The resume text information is parsed using a text algorithm to obtain a job search tag set.

[0075] It can be understood that the above steps can be used to extract the resume text information from the job seeker's resume, and obtain the job-seeking tag set of the job seeker by parsing the resume text information.

[0076] Step S2, obtaining the position text information of the enterprise, and parsing the position text information through a text algorithm to obtain a position tag set;

[0077] The step S2 is specifically as follows:

[0078] Creating an enterprise user interface, allowing enterprises to upload recruitment information through the enterprise user interface;

[0079] Store the recruitment information uploaded by the enterprise in the database to facilitate subsequent analysis of the recruitment information;

[0080] For the uploaded recruitment information, use text extraction tools to convert the recruitment information into plain text format to obtain the position text information;

[0081] The position text information is parsed using a text algorithm to obtain a position label set.

[0082] It can be understood that, through the above steps, it is possible to extract the position text information from the recruitment information of the enterprise, and obtain the position tag set of the enterprise by parsing the position text information.

[0083] Step S3, comparing each job search tag in the job search tag set with each position tag in the position tag set, and comparing the number of tags that match between each job search tag and each position tag;

[0084] The step S3 is specifically as follows:

[0085] Collect job seekers' job tag sets and companies' job tag sets;

[0086] Make sure the labels in both labels are in the same format to facilitate comparison;

[0087] Compare the elements in the two label sets and count the number of overlapping labels to obtain the number of matching labels for the job seekers.

[0088] It can be understood that, through the above steps, it is possible to compare the job-seeking tag set of the job seeker and the position tag set of the enterprise, and to obtain the number of matching tags of the job seeker by counting the number of overlapping tags.

[0089] Step S4, according to the number of tags that match each job-seeking tag and each position tag, eliminating job seekers whose number of tags that match is less than a preset value and pre-admitting job seekers whose number of tags that match is greater than or equal to a preset value;

[0090] The step S4 is specifically as follows:

[0091] Pre-set the threshold for screening the number of matching labels;

[0092] Compare the number of matching labels of the job seeker with a preset matching label number threshold;

[0093] If the number of matching labels of a job seeker is lower than the preset matching label number threshold, the job seeker will be eliminated;

[0094] If the number of matching labels of a job seeker is higher than or equal to the preset matching label number threshold, the job seeker is pre-admitted.

[0095] It can be understood that the above steps can eliminate job seekers whose matching label quantity is lower than the preset matching label quantity threshold, and pre-admit job seekers whose matching label quantity is higher than or equal to the preset matching label quantity threshold.

[0096] Step S5: For job seekers who are pre-admitted with a number of matching tags greater than or equal to a preset value, the matching ratio between the job-seeking tags of the job seekers and the key position tags of the enterprises is compared to determine whether the job seekers meet the final admission conditions.

[0097] The step S5 is specifically as follows:

[0098] Determine which job tags are important in the company's job tag collection to obtain the company's focused job tags;

[0099] Calculate the number of matches between each job search tag in the job search tag set of the pre-admitted job seekers and each of the company's focus position tags;

[0100] The matching ratio is obtained by dividing the number of matches by the total number of job tags of the job seeker;

[0101] The matching ratio is compared with the preset qualification standard to determine whether the job applicant meets the final admission requirements.

[0102] It can be understood that through the above steps, when the number of matches between each job search tag in the job search tag set of the pre-admitted job seeker and each focus position tag of the enterprise reaches the qualified standard, the job seeker can be finally admitted, and when the number of matches between each job search tag in the job search tag set of the pre-admitted job seeker and each focus position tag of the enterprise does not reach the qualified standard, the job seeker can be finally eliminated.

[0103] In the present invention, the accuracy and efficiency of matching resumes with positions can be improved, and a large number of job seekers' resumes can be automatically analyzed and processed to match with recruitment information, thereby greatly reducing the time for enterprise HR to manually screen and match resumes, and improving the efficiency of the overall recruitment process. It can also reduce labor costs and reduce the workload of enterprise HR in the recruitment process, so that enterprise HR can devote more time and energy to more strategic work. And efficient and accurate recruitment methods can help enterprises quickly find job seekers suitable for jobs, shorten the recruitment cycle, and enhance the talent acquisition capabilities and overall competitiveness of enterprises.

[0104] Embodiment 2:

[0105] Figure 2This is a module diagram of the intelligent resume analysis and job matching system of the present invention. The embodiment of the present disclosure also provides a resume matching job matching system, including:

[0106] Client module 10, used for job seekers to upload resumes and companies to upload recruitment information;

[0107] The data storage module 20 is used to store the resumes of job seekers and the recruitment information of enterprises;

[0108] A text extraction module 30, for extracting resume text information from a job seeker's resume and extracting position text information from a company's recruitment information;

[0109] The parsing module 40 is configured with a large language model and a Sentence-BERT model, and is used to parse the resume text information and the position text information to obtain a job application tag set and a position tag set;

[0110] The tag number comparison module 50 is used to compare and calculate the number of tags that match each job tag in the job tag set and each position tag in the position tag set, and eliminate or pre-admit a job seeker based on the number of matching tags;

[0111] The admission calculation module 60 is used to calculate the matching ratio of each job search tag in the job search tag set of the pre-admitted job seeker and each key position tag of the enterprise, so as to determine whether the job seeker meets the final admission conditions.

[0112] Figure 3 1 is a module diagram of the client module 10 of the present invention, wherein the client module 10 further includes:

[0113] The first user interface 101 is used by job seekers to upload resumes;

[0114] The second user interface 102 is used by enterprises to upload recruitment information;

[0115] Ai dialogue interface 103 is used for enterprise HR to conduct artificial intelligence dialogue.

[0116] The above-mentioned Ai dialogue interface 103 is used for enterprise HR to obtain suggestions on interview questions and interview processes for job seekers. Interview questions can be tailored for job seekers by artificial intelligence, and the interview process can be automatically arranged by artificial intelligence according to the schedule information entered by the enterprise. In addition, through the context adjustment method of the above-mentioned language large model, the artificial intelligence possessed by the Ai dialogue interface 103 can conduct relevant questions and answers with HR personnel based on the resume text information of the job seeker and the position text information of the enterprise without special parameter tuning. For example, the Ai dialogue interface 103 can recommend to the enterprise HR whether a job seeker is suitable for the position through the language large model. The enterprise HR can also make the Ai dialogue interface 103 analyze and answer through the language large model after supplementing his own views on the Ai dialogue interface 103. HR can also consult on the Ai dialogue interface 103 when he lacks certain professional knowledge, and the Ai dialogue interface 103 analyzes and answers through the language large model.

[0117] The above-mentioned Large Language Model (LLM) is a type of natural language processing model based on deep learning technology, which aims to understand and generate natural language text. These models are trained on massive text data to learn the structure, semantics and context of the language, so that they can perform a variety of language-related tasks.

[0118] The above-mentioned Sentence-BERT model is a model based on the BERT (Bidirectional Encoder Representations from Transformers) architecture, which is specifically used to generate sentence embedding vectors for sentence-level similarity measurement and other downstream tasks.

[0119] Embodiment 3,

[0120] The present disclosure also provides an electronic device, including:

[0121] one or more processing devices;

[0122] A storage device for storing one or more programs;

[0123] When the one or more programs are executed by the one or more processing devices, the one or more processing devices implement the intelligent resume analysis and job matching method.

[0124] The above-mentioned electronic devices include but are not limited to mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc.

[0125] Embodiment 4:

[0126] The embodiment of the present disclosure also provides a computer-readable storage medium storing a computer program, and when the computer program is executed by one or more processing devices, the method for intelligently analyzing resumes and matching positions is implemented.

[0127] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present disclosure (but not limited to) by each other to form a technical solution.

[0128] In addition, although each operation is described in a specific order, this should not be understood as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0129] Although the subject matter has been described in language specific to structural features and / or methodological logical actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing the claims.

Claims

1. A method for intelligently analyzing resumes and matching positions, comprising the following steps: Step S1, obtaining resume text information of a job seeker, and parsing the resume text information through a text algorithm to obtain a job search tag set; Step S2, obtaining the position text information of the enterprise, and parsing the position text information through a text algorithm to obtain a position tag set; Step S3, comparing each job search tag in the job search tag set with each position tag in the position tag set, and comparing the number of tags that match between each job search tag and each position tag; Step S4, according to the number of tags that match each job-seeking tag and each position tag, eliminating job seekers whose number of tags that match is less than a preset value and pre-admitting job seekers whose number of tags that match is greater than or equal to a preset value; Step S5: For job seekers who are pre-admitted with a number of matching tags greater than or equal to a preset value, the matching ratio between the job-seeking tags of the job seekers and the key position tags of the enterprises is compared to determine whether the job seekers meet the final admission conditions.

2. According to claim 1, the method for intelligently analyzing resumes and matching positions is characterized in that: The step S1 is specifically as follows: Creating a job seeker user interface, allowing job seekers to submit their resumes by uploading documents or directly entering text through the job seeker user interface; Store the resumes uploaded by job seekers in the database to facilitate subsequent analysis of the resumes; For the uploaded resume, use text extraction tools to convert the resume content into plain text format to obtain the resume text information; The resume text information is parsed using a text algorithm to obtain a job search tag set.

3. The method for intelligently analyzing resumes and matching positions according to claim 1 is characterized in that: The step S2 is specifically as follows: Creating an enterprise user interface, allowing enterprises to upload recruitment information through the enterprise user interface; Store the recruitment information uploaded by the enterprise in the database to facilitate subsequent analysis of the recruitment information; For the uploaded recruitment information, use text extraction tools to convert the recruitment information into plain text format to obtain the position text information; The position text information is parsed using a text algorithm to obtain a position label set.

4. The method for intelligently analyzing resumes and matching positions according to claim 1 is characterized in that: The step S3 is specifically as follows: Collect job seekers' job tag sets and companies' job tag sets; Make sure the labels in both labels are in the same format to facilitate comparison; Compare the elements in the two label sets and count the number of overlapping labels to obtain the number of matching labels for the job seekers.

5. According to claim 1, the method for intelligently analyzing resumes and matching positions is characterized in that: The step S4 is specifically as follows: Pre-set the threshold for screening the number of matching labels; Compare the number of matching labels of the job seeker with a preset matching label number threshold; If the number of matching labels of a job seeker is lower than the preset matching label number threshold, the job seeker will be eliminated; If the number of matching labels of a job seeker is higher than or equal to the preset matching label number threshold, the job seeker is pre-admitted.

6. The method for intelligently analyzing resumes and matching positions according to claim 1 is characterized in that: The step S5 is specifically as follows: Determine which job tags are important in the company's job tag collection to obtain the company's focused job tags; Calculate the number of matches between each job search tag in the job search tag set of the pre-admitted job seekers and each of the company's focus position tags; The matching ratio is obtained by dividing the number of matches by the total number of job tags of the job seeker; The matching ratio is compared with the preset qualification standard to determine whether the job applicant meets the final admission requirements.

7. A resume matching job system using the intelligent resume matching job method according to any one of claims 1 to 6, characterized in that: include: Client module, used for job seekers to upload resumes and companies to upload recruitment information; Data storage module, used to store job seekers' resumes and companies' recruitment information; A text extraction module is used to extract resume text information from job seekers' resumes and position text information from companies' recruitment information; The parsing module is configured with a large language model and a Sentence-BERT model, which is used to parse the resume text information and the position text information to obtain the job application tag set and the position tag set; The tag number comparison module is used to compare and calculate the number of matching tags between each job tag in the job tag set and each position tag in the position tag set, and to eliminate or pre-admit a job seeker based on the number of matching tags; The admission calculation module is used to calculate the matching ratio between each job search tag in the job search tag set of the pre-admitted job seekers and each key position tag of the enterprise, so as to determine whether the job seeker meets the final admission conditions.

8. The resume matching job system according to claim 7, characterized in that: The client module also includes: The first user interface is used by job seekers to upload resumes; The second user interface is used by enterprises to upload recruitment information; Ai dialogue interface, used for enterprise HR to conduct artificial intelligence dialogue.

9. An electronic device, characterized in that: include: one or more processing devices; A storage device for storing one or more programs; When the one or more programs are executed by the one or more processing devices, the one or more processing devices implement the intelligent resume analysis and job matching method as described in any one of claims 1-6.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by one or more processing devices, the method for intelligently analyzing resumes and matching positions as described in any one of claims 1 to 6 is implemented.

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