Human resource information processing method and device

Abstract

The invention provides a human resource information processing method and device. The method comprises the following steps: acquiring a plurality of resume data; preprocessing the multiple pieces of resume data to obtain preprocessed resume data; performing feature extraction on the preprocessed resume data to obtain a feature vector; determining a linear combination result according to the feature vector and a model weight of a post matching model; obtaining a first matching result according to the linear combination result and a preset conversion function; performing first screening on the plurality of resume data according to the first matching result and a preset screening value to obtain a first screening result; and according to the first screening result, a preset screening condition and a resume screening model, carrying out second screening on the multiple pieces of resume data to obtain a target screening result. According to the method, the resumes are screened twice, so that the matching degree of the resumes and the target posts can be improved, and the screening efficiency and accuracy of the resumes can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of human resources, and also to a method and device for processing human resources information. Background Art

[0002] In human resource management, resume screening is a very important link. With the development of Internet technology, the number of job seekers submitting resumes through online platforms has increased dramatically, which has put the human resources department under pressure to screen a large number of resumes. Traditional resume screening methods rely too much on keyword matching, resulting in some candidates who are highly matched with the position being missed due to improper use of keywords, or some candidates who do not meet the requirements being mistakenly included in the consideration range due to "keyword stacking". Therefore, how to solve the low accuracy of resume screening has become a technical problem that needs to be solved urgently. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a method and device for processing human resources information.

[0004] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0005] A first aspect of the present invention provides a method for processing human resources information, comprising:

[0006] Get multiple resume data;

[0007] Preprocessing the multiple resume data to obtain preprocessed resume data;

[0008] Performing feature extraction on the preprocessed resume data to obtain a feature vector;

[0009] Determine a linear combination result according to the feature vector and the model weight of the job matching model; the job matching model is obtained by training a preset network model according to the collected historical resume data and the corresponding job matching degree;

[0010] Obtaining a first matching result according to the linear combination result and a preset conversion function;

[0011] Performing a first screening on the plurality of resume data according to the first matching result and a preset screening value to obtain a first screening result;

[0012] The plurality of resume data are screened a second time according to the first screening result, preset screening conditions and the resume screening model to obtain a target screening result.

[0013] Optionally, preprocessing the plurality of resume data to obtain preprocessed resume data includes:

[0014] Performing data cleaning on the plurality of resume data to obtain cleaned data;

[0015] The cleaned data is subjected to word segmentation processing to obtain pre-processed resume data.

[0016] Optionally, feature extraction is performed on the preprocessed resume data to obtain a feature vector, including:

[0017] Extracting keywords from the pre-processed resume data to obtain keywords;

[0018] Performing format conversion on the keywords to obtain word vectors;

[0019] A feature vector is determined according to the keyword and the word vector.

[0020] Optionally, obtaining a first matching result according to the linear combination result and a preset conversion function includes:

[0021] Get a preset conversion function; the preset conversion function is ;

[0022] Obtaining a first matching result according to the linear combination result and the preset conversion function; the first matching result includes a probability value of a matching degree between the resume and the target position;

[0023] in, is the probability value of the match between the resume and the target position, z is the linear combination result, and e is the base of the natural logarithm.

[0024] Optionally, the training process of the job matching model includes:

[0025] Obtain sample data; the sample data includes historical resume data and corresponding job matching degree;

[0026] Training a preset network model with initialized model parameters according to the sample data to obtain a first preset network model;

[0027] Determining target model parameters of the first preset network model according to a preset loss function or a preset number of iterations;

[0028] A job matching model is determined according to the target model parameters.

[0029] Optionally, performing a first screening on the plurality of resume data according to the first matching result and a preset screening value to obtain a first screening result includes:

[0030] Sorting the plurality of resume data according to the first matching result to obtain a sorting result;

[0031] The plurality of resume data are screened for the first time according to the sorting result and the preset screening value to obtain a first screening result.

[0032] Optionally, performing a second screening on the plurality of resume data according to the first screening result, a preset screening condition and a resume screening model to obtain a target screening result includes:

[0033] Obtaining preset screening conditions; the preset screening conditions include educational qualifications and work experience conditions;

[0034] Extract key features from the first screening results according to the preset screening conditions to obtain key feature data;

[0035] The multiple resume data are screened for the second time according to the key feature data and the resume screening model to obtain the target screening result.

[0036] A second aspect of the present invention provides a human resources information processing device, comprising:

[0037] Acquisition module, used to obtain multiple resume data;

[0038] A processing module is used to preprocess the multiple resume data to obtain preprocessed resume data; perform feature extraction on the preprocessed resume data to obtain a feature vector; determine a linear combination result based on the feature vector and the model weight of the job matching model; the job matching model is obtained by training a preset network model based on the collected historical resume data and the corresponding job matching degree; obtain a first matching result based on the linear combination result and a preset conversion function; perform a first screening on the multiple resume data based on the first matching result and a preset screening value to obtain a first screening result; perform a second screening on the multiple resume data based on the first screening result, the preset screening condition and the resume screening model to obtain a target screening result.

[0039] According to a third aspect of the present invention, a computing device is provided, comprising: a processor and a memory storing a computer program, wherein when the computer program is executed by the processor, the method according to the first aspect is executed.

[0040] A fourth aspect of the present invention provides a computer-readable storage medium storing instructions, which, when executed on a computer, causes the computer to execute the method described in the first aspect.

[0041] The above solution of the present invention includes at least the following beneficial effects:

[0042] The above-mentioned scheme of the present invention pre-processes the multiple resume data obtained, then performs feature extraction to obtain feature vectors, determines the linear combination results according to the feature vectors and the model weights of the position matching model, obtains the first matching result according to the linear combination results and the preset conversion function, performs a first screening on the multiple resume data according to the first matching result and the preset screening value to obtain the first screening result, and then performs a second screening on the multiple resume data according to the first screening result, the preset screening conditions and the resume screening model to obtain the target screening result. Through the two screenings, the matching degree between the obtained resume and the target position can be improved, which is beneficial to improving the screening efficiency and accuracy of the resumes. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 is a flowchart of a method for processing human resources information in an embodiment of the present invention;

[0044] Figure 2 It is a schematic diagram of the structure of a device for processing human resources information in an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present invention and to enable the scope of the present invention to be fully communicated to those skilled in the art.

[0046] like Figure 1 As shown, an embodiment of the present invention provides a method for processing human resources information, comprising the following steps:

[0047] Step 101, obtaining multiple resume data;

[0048] Step 102, preprocessing the plurality of resume data to obtain preprocessed resume data;

[0049] Step 103, extracting features from the pre-processed resume data to obtain feature vectors;

[0050] Step 104, determining a linear combination result according to the feature vector and the model weight of the job matching model; the job matching model is obtained by training a preset network model according to the collected historical resume data and the corresponding job matching degree;

[0051] Step 105, obtaining a first matching result according to the linear combination result and a preset conversion function;

[0052] Step 106: Perform the first screening on the multiple resume data according to the first matching result and a preset screening value to obtain a first screening result;

[0053] Step 107: Perform the second screening on the multiple resume data according to the first screening result, a preset screening condition, and a resume screening model to obtain a target screening result.

[0054] In the method for processing human resource information according to the embodiment of the present invention, by preprocessing the obtained multiple resume data, then performing feature extraction to obtain feature vectors, determining a linear combination result according to the feature vectors and the model weights of the job matching model, obtaining a first matching result according to the linear combination result and a preset conversion function, performing the first screening on the multiple resume data according to the first matching result and a preset screening value to obtain a first screening result, and then performing the second screening on the multiple resume data according to the first screening result, a preset screening condition, and a resume screening model to obtain a target screening result. Through the two screenings, the matching degree between the obtained resumes and the target job can be improved, which is beneficial to improving the screening efficiency and accuracy of the resumes.

[0055] In an optional embodiment of the present invention, the obtaining of the multiple resume data in step 101 may be obtaining the multiple resume data from channels such as recruitment platforms and internal databases. The resume data may include information such as personal basic information, work experience, education level, and work skills.

[0056] In an optional embodiment of the present invention, step 102 includes:

[0057] Step 1021: Clean the multiple resume data to obtain cleaned data;

[0058] Specifically, data cleaning is to remove irrelevant characters, HTML (HyperText Markup Language) tags, special symbols, etc. from the resume data to ensure the purity of the text data and improve the subsequent processing efficiency.

[0059] Step 1022: Perform word segmentation on the cleaned data to obtain preprocessed resume data.

[0060] Specifically, use a pre-stored dictionary to cut the text in the cleaned data into independent lexical units to obtain a first word segmentation result; delete the words in a preset stop word list from the word segmentation result, such as "de", "shi", "zai", etc., to obtain a second word segmentation result; recombine the second word segmentation result into text to obtain preprocessed resume data for subsequent processing. The purpose of performing word segmentation is to provide high-quality data input for subsequent processing, which is beneficial to improving the processing efficiency and the accuracy of the result.

[0061] In an optional embodiment of the present invention, step 103 includes:

[0062] Step 1031, extracting keywords from the pre-processed resume data to obtain keywords;

[0063] Specifically, through Count the frequency of each word in a single preprocessed resume data; then count the occurrence of each word in all preprocessed resume data and calculate the inverse document frequency ; Then multiply the TF and IDF of each word to get the TF-IDF value; Finally, sort the words according to the TF-IDF value and extract the top-ranked words as keywords.

[0064] Step 1032, convert the format of the keyword to obtain a word vector;

[0065] Specifically, keywords can be input into the trained word embedding model for format conversion. The word embedding model processes keywords as follows: In the model, each keyword q corresponds to a word vector , where a is the embedding dimension (e.g. a can be 300), and the word vector Through the weight matrix of the word embedding model Extraction is obtained, and the calculation formula is v q =Q[q] , where c is the size of the vocabulary and the word vector of keyword q is is the qth row of the weight matrix Q.

[0066] Step 1033: Determine a feature vector based on the keyword and the word vector.

[0067] Specifically, you can The feature vector is calculated, where F is the feature vector and n is the number of keywords. is the word vector of the ith keyword. The feature vector is used for subsequent model input.

[0068] In an optional embodiment of the present invention, step 104 includes:

[0069] Step 10411, based on the feature vector, the model weight of the job matching model and , determine the linear combination result.

[0070] Specifically, the feature vector and the model weight of the job matching model can be input into , and obtain the linear combination result z, where F is the feature vector, w is the target weight vector in the model weight, and b is the target bias term in the model weight.

[0071] In an optional embodiment of the present invention, the training process of the job matching model in step 104 includes:

[0072] Step 10421, obtaining sample data; the sample data includes historical resume data and corresponding job matching degree;

[0073] Specifically, the sample data can be resume data and the corresponding job matching degree within a preset period of time in history. The specific value of the preset period of time can be set as needed, such as within the past year or the past three years, etc. Obtaining sample data can be used for subsequent model training.

[0074] Step 10422, training a preset network model with initialized model parameters according to the sample data to obtain a first preset network model;

[0075] Step 10423, determining target model parameters of the first preset network model according to a preset loss function or a preset number of iterations;

[0076] Specifically, the preset network model initializes the weight vector w and the bias term b to zero or a small random number, and then inputs the sample data into the initialized preset network model for training to obtain a first preset network model. If the preset loss function of the first preset network model converges to a smaller value (such as a preset convergence value) or reaches a preset number of iterations, the model parameters of the first preset network model are determined to be the target model parameters.

[0077] Among them, the preset loss function is L w,b =- 1 m ∑ i=1 m [ y i log y i ̂ +(1- y i ) log 1- y i ̂ ] , where m is the number of samples in the sample data, w is the weight vector, and b is the bias term. is the predicted probability, is the true degree of match.

[0078] Step 10424, determine the job matching model based on the target model parameters.

[0079] Specifically, the target model parameters are input into the preset network model to determine the job matching model.

[0080] In an optional embodiment of the present invention, step 105 includes:

[0081] Step 1051, obtaining a preset conversion function; the preset conversion function is ;

[0082] Step 1052, obtaining a first matching result based on the linear combination result and the preset conversion function; the first matching result includes a probability value of a matching degree between the resume and the target position.

[0083] in, is the probability value of the match between the resume and the target position, z is the linear combination result, and e is the base of the natural logarithm.

[0084] Specifically, through the formula Get the first matching result, where It is the probability value of the match between the resume and the target position output by the preset conversion function, indicating the probability of the resume matching the position, between 0 and 1, z is the linear combination result, and e is the base of the natural logarithm (approximately equal to 2.71828). Here, the first matching result includes each resume data and its corresponding probability value, which is convenient for subsequent sorting and screening.

[0085] In an optional embodiment of the present invention, step 106 includes:

[0086] Step 1061, sorting the plurality of resume data according to the first matching result to obtain a sorting result;

[0087] Specifically, the resume data may be sorted from high to low according to the probability value in the first matching result to obtain a sorting result, which includes a sorting sequence number, a corresponding probability value and resume data.

[0088] Step 1062, perform a first screening on the plurality of resume data according to the sorting result and the preset screening value to obtain a first screening result.

[0089] Specifically, the preset filtering value can be the number of selected resumes. For example, in a specific embodiment, the preset filtering value is 5, and the data of the first five resumes in the sorting result are extracted. The first filtering result includes the data of the first five resumes, the sorting sequence numbers, the corresponding probability values, etc.

[0090] In an optional embodiment of the present invention, step 107 includes:

[0091] Step 1071, obtaining preset screening conditions; the preset screening conditions include educational background conditions and work experience conditions;

[0092] Specifically, the academic qualifications, work experience, education level, professional skills, etc. required for the target position can be obtained for further screening of resume data to improve the final screening accuracy. In a specific embodiment, the preset screening conditions include: the academic qualification is a bachelor's degree, the work experience is at least one year, and the professional skills is a level 6 English certificate. The preset screening conditions can be set according to the specific job requirements.

[0093] Step 1072, extracting key features from the first screening results according to the preset screening conditions to obtain key feature data;

[0094] Specifically, if the preset screening conditions include academic qualifications, work experience conditions, education level conditions, professional skills conditions, etc., the academic qualifications, work experience conditions, education level conditions, and professional skills conditions in the resume in the first screening result are respectively coded and processed to obtain numerical data. For example, if the academic qualification in a resume is a bachelor's degree, the corresponding numerical data is 1. If the academic qualification is a master's degree, the corresponding numerical data is 2, etc. The key feature data includes the numerical data corresponding to the preset screening conditions. In a specific embodiment, the key feature data includes 1 (the academic qualification is a bachelor's degree), 0.1 (the work experience is 1 year), and 2 (the professional skills condition is to have an English level 6 certificate).

[0095] Step 1073, performing a second screening on the multiple resume data according to the key feature data and the resume screening model to obtain a target screening result.

[0096] Specifically, key feature data is input into the resume screening model, using the formula , , and obtain the probability value of the resume data meeting the preset screening conditions. If the probability value of the resume data meeting the preset screening conditions is greater than the preset screening probability value, it can be determined as the target resume. is the decision function, is the weight vector of the resume screening model, T is the transposition operation, is the bias term of the resume screening model, It is the mapping of key feature data in high-dimensional space. is the probability value that the resume data meets the preset screening conditions, and A and B are fitting parameters (in a specific embodiment, A is -0.1 and B is 0.5).

[0097] Here, the target screening result may include the target resume data, the probability value in the first screening result, the probability value that the resume data meets the preset screening conditions, etc. The training process of the resume screening model includes:

[0098] Collect historical resume data; each resume data is marked with whether it meets the preset screening conditions (such as 1 for compliance and 0 for non-compliance).

[0099] The historical resume data is cleaned and converted to a sample set; the purpose of data cleaning is to improve the accuracy of the training set and the effectiveness of subsequent model training. Among them, data format conversion is to convert non-numeric data in the historical resume data into numerical data.

[0100] Divide the sample set into a training set and a test set; 80% of the sample set can be divided into a training set and 20% into a test set.

[0101] Initialize the parameters of the preset network model; this may include: determining the kernel function as , set the regularization parameter C to 1.0, where is the key feature data of the i-th historical resume data, The key feature data of the jth historical resume data, T represents the transposition operation, is the kernel function, which is used to transform and Mapping to a high-dimensional space.

[0102] Use the training set to train the initialized preset network model to obtain the trained model; then use the test set and the preset verification index to verify the performance of the trained model. If the verification index reaches the preset index value, the trained model will be used as the resume screening model. The preset verification index can be: accuracy and / or recall ,in, is the accuracy, is the recall rate, TP is the number of samples that are actually positive and predicted as positive by the model, TN is the number of samples that are actually negative and predicted as negative by the model, FP is the number of samples that are actually negative but predicted as positive by the model (false positives), and FN is the number of samples that are actually positive but predicted as negative by the model (missed positives).

[0103] In an optional embodiment of the present invention, the method further comprises:

[0104] Step 108, presenting the target screening result in a visual manner.

[0105] Specifically, the target screening results can be converted according to a preset presentation type (such as a chart type), so that human resource managers or recruiters can intuitively see the results of resume screening and make recruitment decisions based on scoring and sorting.

[0106] A specific embodiment of the method for processing human resources information of the embodiment of the present invention includes:

[0107] Step 111, obtaining multiple resume data;

[0108] Multiple resume data obtained from recruitment platforms, internal databases and other channels for subsequent screening.

[0109] Step 112, preprocessing;

[0110] Perform data cleaning and word segmentation on multiple resume data to obtain pre-processed resume data to improve data accuracy and subsequent processing efficiency.

[0111] Step 113, feature extraction;

[0112] By extracting keywords and calculating word vectors from the preprocessed resume data, the feature vector is determined to facilitate subsequent model input and processing.

[0113] Step 114, calculating the job matching probability;

[0114] The trained job matching model is used to calculate the job matching probability value for each resume data, providing a basis for subsequent resume screening, which is conducive to saving manpower and improving screening efficiency.

[0115] Step 115, first screening;

[0116] First, the resume data are sorted from high to low according to the probability value in the first matching result to obtain the sorting result, and then the resume data in the top N items in the sorting result are filtered out according to the preset filtering value as the first filtering result, where N is less than or equal to the preset filtering value.

[0117] Step 116, second screening.

[0118] During the second screening, the resume data in the first screening results needs to be further screened according to the specific conditions required by the target position to improve the matching degree between the resume and the target position. The resume data finally screened is the target screening result, and the target screening result is presented in a preset visual way for human resource managers or recruiters to view.

[0119] The method for processing human resources information of the embodiment of the present invention saves the time of recruiters in selecting personnel matching the target position from a large amount of resume data by multiple screening of the obtained multiple resume data, which is beneficial to saving labor costs and improving resume screening efficiency.

[0120] like Figure 2 As shown, the embodiment of the present invention provides a human resources information processing device 200, comprising:

[0121] An acquisition module 201 is used to acquire multiple resume data;

[0122] Processing module 202 is used to preprocess the multiple resume data to obtain preprocessed resume data; perform feature extraction on the preprocessed resume data to obtain a feature vector; determine a linear combination result based on the feature vector and the model weight of the job matching model; the job matching model is obtained by training a preset network model based on the collected historical resume data and the corresponding job matching degree; obtain a first matching result based on the linear combination result and a preset conversion function; perform a first screening on the multiple resume data based on the first matching result and a preset screening value to obtain a first screening result; perform a second screening on the multiple resume data based on the first screening result, the preset screening condition and the resume screening model to obtain a target screening result.

[0123] Optionally, preprocessing the plurality of resume data to obtain preprocessed resume data includes:

[0124] Performing data cleaning on the plurality of resume data to obtain cleaned data;

[0125] The cleaned data is subjected to word segmentation processing to obtain pre-processed resume data.

[0126] Optionally, feature extraction is performed on the preprocessed resume data to obtain a feature vector, including:

[0127] Extracting keywords from the pre-processed resume data to obtain keywords;

[0128] Performing format conversion on the keywords to obtain word vectors;

[0129] A feature vector is determined according to the keyword and the word vector.

[0130] Optionally, obtaining a first matching result according to the linear combination result and a preset conversion function includes:

[0131] Get a preset conversion function; the preset conversion function is ;

[0132] Obtaining a first matching result according to the linear combination result and the preset conversion function; the first matching result includes a probability value of a matching degree between the resume and the target position;

[0133] in, is the probability value of the match between the resume and the target position, z is the linear combination result, and e is the base of the natural logarithm.

[0134] Optionally, the training process of the job matching model includes:

[0135] Obtain sample data; the sample data includes historical resume data and corresponding job matching degree;

[0136] Training a preset network model with initialized model parameters according to the sample data to obtain a first preset network model;

[0137] Determining target model parameters of the first preset network model according to a preset loss function or a preset number of iterations;

[0138] A job matching model is determined according to the target model parameters.

[0139] Optionally, performing a first screening on the plurality of resume data according to the first matching result and a preset screening value to obtain a first screening result includes:

[0140] Sorting the plurality of resume data according to the first matching result to obtain a sorting result;

[0141] The plurality of resume data are screened for the first time according to the sorting result and the preset screening value to obtain a first screening result.

[0142] Optionally, performing a second screening on the plurality of resume data according to the first screening result, a preset screening condition and a resume screening model to obtain a target screening result includes:

[0143] Obtaining preset screening conditions; the preset screening conditions include educational qualifications and work experience conditions;

[0144] Extract key features from the first screening results according to the preset screening conditions to obtain key feature data;

[0145] The multiple resume data are screened for the second time according to the key feature data and the resume screening model to obtain the target screening result.

[0146] The human resources information processing device of the embodiment of the present invention pre-processes the obtained multiple resume data, then performs feature extraction to obtain a feature vector, determines a linear combination result based on the feature vector and the model weight of the position matching model, obtains a first matching result based on the linear combination result and a preset conversion function, performs a first screening on the multiple resume data based on the first matching result and the preset screening value to obtain a first screening result, and then performs a second screening on the multiple resume data based on the first screening result, the preset screening condition and the resume screening model to obtain a target screening result. Through the two screenings, the matching degree between the obtained resume and the target position can be improved, which is conducive to improving the efficiency and accuracy of resume screening.

[0147] It should be noted that the device is a device corresponding to the above method, and all implementations in the above method embodiment are applicable to the embodiment of the device and can achieve the same technical effect, which will not be described in detail in this embodiment.

[0148] The embodiment of the present invention further provides a computing device, comprising: a processor, and a memory storing a computer program, wherein when the computer program is executed by the processor, the method described in any one of the above embodiments is executed. All implementations in the above method embodiments are applicable to the embodiments of the device, and can achieve the same technical effects. They will not be described in detail in this embodiment.

[0149] The embodiment of the present invention further provides a computer-readable storage medium on which instructions are stored. When the instructions are executed on a computer, the computer executes the method as described in any one of the above embodiments. All implementations in the above method embodiments are applicable to the embodiments of the device and can achieve the same technical effects. They will not be described in detail in this embodiment.

[0150] It should be noted that in the apparatus and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent solutions of the present invention. In addition, the steps of performing the above series of processes can naturally be performed in chronological order according to the order of description, but do not necessarily need to be performed in chronological order. Some steps can be performed in parallel, crosswise or independently of each other.

[0151] It should be noted that, in the above-mentioned embodiments, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "include one..." do not exclude the existence of other identical elements in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the methods and devices in the implementation of the above-mentioned embodiments is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in the opposite order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0152] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for processing human resources information, characterized in that: include: Get multiple resume data; Preprocessing the multiple resume data to obtain preprocessed resume data; Performing feature extraction on the preprocessed resume data to obtain a feature vector; Determine a linear combination result according to the feature vector and the model weight of the job matching model; the job matching model is obtained by training a preset network model according to the collected historical resume data and the corresponding job matching degree; Obtaining a first matching result according to the linear combination result and a preset conversion function; Performing a first screening on the plurality of resume data according to the first matching result and a preset screening value to obtain a first screening result; The plurality of resume data are screened a second time according to the first screening result, preset screening conditions and the resume screening model to obtain a target screening result.

2. The method for processing human resources information according to claim 1, characterized in that: Preprocessing the plurality of resume data to obtain preprocessed resume data includes: Performing data cleaning on the plurality of resume data to obtain cleaned data; The cleaned data is subjected to word segmentation processing to obtain pre-processed resume data.

3. The method for processing human resources information according to claim 1, characterized in that: Feature extraction is performed on the preprocessed resume data to obtain a feature vector, including: Extracting keywords from the pre-processed resume data to obtain keywords; Performing format conversion on the keywords to obtain word vectors; A feature vector is determined according to the keyword and the word vector.

4. The method for processing human resource information according to claim 1, characterized in that: Obtaining a first matching result according to the linear combination result and a preset conversion function includes: Get a preset conversion function; the preset conversion function is ; Obtaining a first matching result according to the linear combination result and the preset conversion function; the first matching result includes a probability value of a matching degree between the resume and the target position; in, is the probability value of the match between the resume and the target position, z is the linear combination result, and e is the base of the natural logarithm.

5. The method for processing human resources information according to claim 1, characterized in that: The training process of the job matching model includes: Obtain sample data; the sample data includes historical resume data and corresponding job matching degree; Training a preset network model with initialized model parameters according to the sample data to obtain a first preset network model; Determining target model parameters of the first preset network model according to a preset loss function or a preset number of iterations; A job matching model is determined according to the target model parameters.

6. The method for processing human resource information according to claim 1, characterized in that: The plurality of resume data are screened for the first time according to the first matching result and the preset screening value to obtain a first screening result, including: Sorting the plurality of resume data according to the first matching result to obtain a sorting result; The plurality of resume data are screened for the first time according to the sorting result and the preset screening value to obtain a first screening result.

7. The method for processing human resource information according to claim 1, characterized in that: The plurality of resume data are screened for a second time according to the first screening result, the preset screening condition and the resume screening model to obtain a target screening result, including: Obtaining preset screening conditions; the preset screening conditions include educational qualifications and work experience conditions; Extract key features from the first screening results according to the preset screening conditions to obtain key feature data; The multiple resume data are screened for the second time according to the key feature data and the resume screening model to obtain the target screening result.

8. A human resources information processing device, characterized in that: include: Acquisition module, used to obtain multiple resume data; A processing module, used for preprocessing the plurality of resume data to obtain preprocessed resume data; Perform feature extraction on the pre-processed resume data to obtain a feature vector; determine a linear combination result based on the feature vector and the model weight of the job matching model; the job matching model is obtained by training a preset network model based on the collected historical resume data and the corresponding job matching degree; Obtaining a first matching result according to the linear combination result and a preset conversion function; Performing a first screening on the plurality of resume data according to the first matching result and a preset screening value to obtain a first screening result; The plurality of resume data are screened a second time according to the first screening result, preset screening conditions and the resume screening model to obtain a target screening result.

9. A computing device, characterized in that include: A processor and a memory storing a computer program, wherein when the computer program is executed by the processor, the method according to any one of claims 1 to 7 is performed.

10. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer is caused to execute the method according to any one of claims 1 to 7.

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