College graduate employment direction prediction method and device

Abstract

The invention provides a university graduate employment direction prediction method and device, and relates to the technical field of employment direction prediction, and the method comprises the steps: collecting the professional class weighted average score, the public class weighted average score, the occupational character type and the library access frequency of the previous graduates; calculating an employment direction success coefficient, an employment direction satisfaction coefficient and an employment direction prediction coefficient of the previous graduates; and comparing and judging the employment direction prediction coefficient with an employment direction evaluation threshold so as to determine a recommended employment direction and recommend the recommended employment direction to graduates. According to the invention, comprehensive three-dimensional analysis is carried out on the graduates from four dimensions of the weighted average score of the professional class, the weighted average score of the public class, the occupational character type and the number of times of entry and exit of the library; and an important reference factor of employment direction satisfaction of previous students is introduced, so that the precision and practicability of graduate employment direction prediction are improved.

Description

Technical Field

[0001] This invention relates to the field of employment direction prediction technology, specifically to a method and apparatus for predicting the employment direction of college graduates. Background Technology

[0002] With the continuous expansion of education, more and more families have realized the importance of knowledge. This has led to a surge in university enrollment, resulting in a growing number of college students. However, after graduation, students face difficult choices. Many college students feel lost and uncertain about how to apply their professional knowledge, how to choose employment, job prospects, whether to consider postgraduate studies or applying for public institutions, and so on. While some universities compile annual statistics on the employment situation of their graduates, such as the overall employment rate and the general career paths of students, this data only applies to a specific major or all graduates of that year. Such statistics do not provide much guidance or assistance to graduating students. Therefore, how to predict the career path of each graduating student, thereby minimizing their pre-graduation confusion and anxiety, has become an urgent problem to solve.

[0003] In the prior art, a method, apparatus, system, and storage medium for online prediction of college students' employment is disclosed in publication number "CN110059883A". The method includes: acquiring all courses taken by the student during their studies, the academic performance of each course, the credits for each course, and the student's identity information; classifying all courses taken by the student to obtain course categories; obtaining the student's overall score in the first course category based on the courses, academic performance, and credits belonging to that category; grouping all students' overall scores in the first course category according to a preset classification dimension; assigning group labels; inputting the student's identity information, course category identifiers, and group labels for each course category into a pre-constructed student employment direction prediction model to predict the student's employment direction; and obtaining and displaying the prediction results to the student. This method predicts students' post-graduation employment directions, providing each student with a reference for their post-graduation employment choices and minimizing their pre-graduation confusion and anxiety.

[0004] However, existing technologies still have significant shortcomings. For example, relying solely on academic performance to predict students' employment paths is too one-sided and arbitrary. Factors influencing employment include not only academic performance but also intrinsic factors such as personal character and habits. Therefore, the results obtained using existing technologies are prone to error. Furthermore, when building predictive models using data from previous graduates, existing technologies fail to consider the crucial factor of whether previous graduates are satisfied with their choices. If a large proportion of previous graduates are dissatisfied with their decisions, the resulting predictive models will not only fail to help current graduates but may even mislead them. Summary of the Invention

[0005] The purpose of this invention is to provide a method and apparatus for predicting the employment direction of college graduates, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A method for predicting the employment direction of college graduates includes the following steps:

[0008] S1, collect the weighted average score of the major courses of the graduating class. i Weighted average score of public courses (b) i Occupational personality type c i Number of times entering and exiting the library (d) i Where i represents the ID of different graduating students, i = 1, 2, 3, ..., n;

[0009] S2, collect the number N of previous graduates who have found employment by selecting different employment directions. j The data includes the weighted average scores of professional courses, the weighted average scores of general courses, career personality types, and library visits of previous graduates who chose different employment directions. Combined with the data collected in step S1, the employment success coefficient (JYCG) of the current year's graduates is calculated. j , where j represents the number of different employment directions, j = 1, 2, 3, ..., m;

[0010] S3: Collect satisfaction data from previous graduates who selected different employment directions, and combine this data with the data collected in steps S1 and S2 to calculate the employment direction satisfaction coefficient JYMY for current graduates. j ;

[0011] S4, based on the success rate of employment direction JYCG j and the job satisfaction coefficient JYMY j Generate employment direction prediction coefficient JYYC j ;

[0012] S5, the employment direction prediction coefficient JYYC j The results are compared with the employment direction evaluation threshold TTH. If JYYC is met, the result is considered satisfactory. j If the employment direction prediction coefficient is ≥TTH, then the employment direction prediction coefficient JYYC will be used. j The corresponding employment direction will be recommended to the graduate.

[0013] As a preferred option, the occupational personality types are divided into four categories: extraversion, agreeableness, stability, and openness.

[0014] As a preferred option, the number N of previous graduates employed in different career paths is collected. j The data includes the weighted average scores of professional courses, the weighted average scores of general courses, career personality types, and library visits of previous graduates who chose different employment directions. Combined with the data collected in step S1, the employment success coefficient (JYCG) of the current year's graduates is calculated. j Where j represents the number of different employment directions, j = 1, 2, 3, ..., m, the specific logic is as follows:

[0015] S21, Collect the number N of previous graduates who chose different employment directions. j The data also includes the weighted average scores of professional courses, the weighted average scores of public courses, career personality types, and library visits of previous graduates who chose different career paths.

[0016] S22 uses the grades of weighted average scores of professional courses and weighted average scores of public courses to classify them as excellent, good, pass, and fail, and uses the grades of library visits to classify them as excellent, good, and average.

[0017] S23. Based on the weighted average scores of professional courses, weighted average scores of general courses, career personality types, and library visit frequency data of previous graduates who chose different employment directions, determine the grade range DJa of the weighted average score of professional courses for each employment direction with the largest number of previous graduates. j And the number of previous graduates in that range, Na j Weighted average grade range for public courses (DJb) j and the number of previous graduates Nb in this range j DJc's Occupational Personality Type j And the number of previous graduates of this occupational personality type Nc j Library visit frequency range DJd j and the number of previous graduates in this range Nd j ;

[0018] S24. Based on the data collected in steps S1 and S23, calculate the employment success coefficient (JYCG) for recent graduates. j The calculation formula is as follows:

[0019]

[0020] Where, when a i At or above DJa j When the interval value is , f(a) i DJA j ) equals 1, when a i Below DJa j When the interval value is , f(a) i DJA j ) equals 0;

[0021] Among them, when b i At or above DJb j When the interval value is , f(b) i DJb j When b equals 1, i Below DJb j When the interval value is , f(b) i DJb j ) equals 0;

[0022] Where, when c i and DJc j When the occupational personality types are the same, f(c i DJc j ) equals 1, when c i and DJc j When occupational personality types are different, f(c) i DJc j ) equals 0;

[0023] Where, when d i At or above DJd j When the interval value is , f(d) i DJd j ) equals 1, when d i Below DJd j When the interval value is , f(d) i DJd j ) equals 0;

[0024] Wherein, λ1, λ2, λ3, and λ4 are all preset proportional coefficients greater than 0, and λ1+λ2+λ3+λ4=1.

[0025] As a preferred option, the employment directions are divided into civil servant, public institution employee, state-owned enterprise, private enterprise, foreign enterprise, and further education.

[0026] As a preferred option, the specific logic for classifying the weighted average grades of professional courses and general courses into excellent, good, pass, and fail categories, and for classifying the frequency of library visits into excellent, good, and average categories, is as follows:

[0027] Firstly, the grading of the weighted average score for professional courses is as follows:

[0028] When the weighted average score of professional courses is between 85 and 100, it is classified as excellent.

[0029] When the weighted average score of professional courses is between 70 and 84, it is classified as a good grade.

[0030] When the weighted average score of professional courses is between 60 and 69, it is classified as a passing grade.

[0031] When the weighted average score of professional courses is below 60, it is classified as failing.

[0032] Secondly, the grade classification for the weighted average score of public courses is as follows:

[0033] When the weighted average score of public courses is between 85 and 100, it is classified as excellent.

[0034] When the weighted average score of public courses is between 70 and 84, it is classified as a good grade.

[0035] When the weighted average score of public courses is between 60 and 69, it is classified as a passing grade.

[0036] When the weighted average score of public courses is below 60, it is classified as failing.

[0037] Thirdly, the library's access frequency is categorized as follows:

[0038] If a user enters or exits the library more than 600 times, they are classified as an excellent user.

[0039] When the number of visits to the library is between 300 and 600, it is classified as a good range;

[0040] If a user enters or exits the library less than 300 times, the user is classified as belonging to the general range.

[0041] As a preferred option, the satisfaction data of previous graduates with different employment directions is collected and combined with the data collected in steps S1 and S2 to calculate the employment direction satisfaction coefficient JYMY of the current graduates. j The specific logic is as follows:

[0042] S31. Collect satisfaction data of previous graduates for different employment directions, and determine the average satisfaction of previous graduates for each employment direction. And determine that among the various employment directions, a i The number of previous graduates Na′ in the weighted average grade range of their major courses j And the average satisfaction score of previous graduates in this range, MYa j b i The number of previous graduates Nb' in the weighted average grade range of their general education courses j And the average satisfaction score of previous graduates in this range, MYb j c i The number of previous graduates belonging to the corresponding occupational personality type Nc' j And the average satisfaction score (MYc) of previous graduates of this personality type. j d i The number of previous graduates in the library's visit frequency range Nd' j And the average satisfaction score of previous graduates in this range (MYd) j ;

[0043] S32, based on the data from step S31 and N from step S2 j Calculate the job satisfaction coefficient of recent graduates JYMY j The calculation formula is as follows:

[0044]

[0045] Among them, θ1, θ2, θ3, θ4, and θ5 are all preset proportional coefficients greater than 0, and θ1+θ2+θ3+θ4+θ5=1.

[0046] As a preferred option, the satisfaction level of previous graduates can be evaluated using a score scale of 1-10, where 1 represents the worst and 10 represents the best.

[0047] As a preferred option, the employment direction prediction coefficient JYYC j The calculation formula is as follows:

[0048]

[0049] Wherein, ω1, ω2, f1, and f2 are all preset proportional coefficients greater than 0, and 0 < ω1 + ω2 < 1, and f1 + f2 = 1.

[0050] A device for predicting the employment direction of college graduates, used in the aforementioned method for predicting the employment direction of college graduates, includes:

[0051] Data collection unit for recent graduates: used to collect the weighted average score of professional courses for recent graduates. i Weighted average score of public courses (b) i Occupational personality type c i Number of times entering and exiting the library (d) i Where i represents the ID of different graduating students, i = 1, 2, 3, ..., n;

[0052] Employment Direction Success Coefficient Calculation Unit: Used to collect the number N of previous graduates who have found employment after choosing different employment directions. j The data includes the weighted average scores of professional courses, the weighted average scores of general courses, career personality types, and library visits of previous graduates who chose different employment directions. Combined with the data collected in step S1, the employment success coefficient (JYCG) of the current year's graduates is calculated. j , where j represents the number of different employment directions, j = 1, 2, 3, ..., m;

[0053] Job Direction Satisfaction Coefficient Calculation Unit: This unit collects satisfaction data from previous graduates who selected different job directions, and combines this data with the data collected in steps S1 and S2 to calculate the job direction satisfaction coefficient JYMY for current graduates. j ;

[0054] Employment Direction Prediction Coefficient Calculation Unit: Used to calculate the employment direction success coefficient JYCG j and the job satisfaction coefficient JYMY j Generate employment direction prediction coefficient JYYC j ;

[0055] Recommended Employment Direction Generation Unit: Used to generate employment direction prediction coefficients JYYC j The results are compared with the employment direction evaluation threshold TTH. If JYYC is met, the result is considered satisfactory. j If the employment direction prediction coefficient is ≥TTH, then the employment direction prediction coefficient JYYC will be used. j The corresponding employment direction will be recommended to the graduate.

[0056] Compared with the prior art, the beneficial effects of the present invention are:

[0057] The present invention provides a method and apparatus for predicting the employment direction of college graduates. Compared with the prior art, which only relies on academic performance to predict employment direction, this invention provides a comprehensive and three-dimensional analysis of graduates from four dimensions: weighted average grades of professional courses, weighted average grades of public courses, career personality type, and frequency of library visits. This improves the accuracy of the prediction of graduates' employment direction. Furthermore, by introducing the important reference factor of previous graduates' satisfaction with their employment direction, it ensures that the final prediction results are beneficial to graduates rather than having a negative effect that misleads them, thus providing practicality for employment direction prediction. Attached Figure Description

[0058] Figure 1 This is a flowchart of the method for predicting the employment direction of college graduates in this invention;

[0059] Figure 2 This is a module unit diagram of the college graduate employment direction prediction device in this invention. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0061] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0062] Example 1:

[0063] Please see Figure 1 This invention provides a method for predicting the employment direction of college graduates, comprising the following steps:

[0064] S1, collect the weighted average score of the major courses of the graduating class. i Weighted average score of public courses (b) i Occupational personality type c i Number of times entering and exiting the library (d) iWhere i represents the ID number of different graduating students, i = 1, 2, 3, ..., n. The student ID number can be used to distinguish between different graduating students, as detailed below:

[0065] Among them, the weighted average score of professional courses a i It reflects the graduate's actual mastery and adaptability in the professional field, and is an important reference indicator for evaluating the graduate's academic level in the professional field. It is also an important influencing factor in predicting the graduate's employment direction. The weighted average grade (a) of the professional courses of the graduating class i This information can be obtained through the school's academic affairs office website. The specific method involves first obtaining the graduate's grades for each major course during their university studies, along with the corresponding credits for each course. Then, a weighted average grade (a) is calculated based on these grades and credits. i The calculation formula is as follows:

[0066]

[0067] Among them, the weighted average score of public courses b i It reflects the comprehensive abilities and knowledge level of graduates in interdisciplinary fields, and is an important reference indicator for evaluating the academic level of graduates in public disciplines. It is also an important influencing factor in predicting the employment direction of graduates. The weighted average grade of public courses for graduating students is b. i This information can be obtained through the school's academic affairs office website. The specific method involves first obtaining the graduate's grades for each general education course during their university studies, along with the corresponding credits. Then, a weighted average grade (b) is calculated based on these grades and credits. i The calculation formula is as follows:

[0068]

[0069] Among them, occupational personality type c i It has a profound impact on graduates' adaptability, work performance, and job satisfaction in specific professional environments. It is an important reference indicator for graduates to choose their employment direction and a significant factor in predicting graduates' employment direction. The career personality type of recent graduates (c) i The results can be obtained through the MBTT occupational personality scale filled out by graduates. The MBTT can classify the occupational personality types of recent graduates into four categories: extraversion, agreeableness, stability, and openness.

[0070] Among them, the number of times the library was entered and exited d i This reflects the graduate's initiative and self-discipline in independent learning, and is an important influencing factor in predicting the graduate's employment direction. The number of times the graduating student visits the library (d) iThis data can be obtained by graduating students logging into the university library website. The university library website records the time and number of times the graduate enters and exits the library. This data is generally obtained by having the graduate swipe their student card to enter and exit the library during their university years.

[0071] S2, collect the number N of previous graduates who have found employment by selecting different employment directions. j The data includes the weighted average scores of professional courses, the weighted average scores of general courses, career personality types, and library visits of previous graduates who chose different employment directions. Combined with the data collected in step S1, the employment success coefficient (JYCG) of the current year's graduates is calculated. j ;

[0072] Where j represents the number of different employment directions, j = 1, 2, 3, ..., m, and employment directions can be divided into civil servant, public institution employee, state-owned enterprise, private enterprise, foreign enterprise, and further education, etc.

[0073] Step S2 includes the following steps:

[0074] S21, Collect the number N of previous graduates who chose different employment directions. j The data also includes the weighted average scores of professional courses, the weighted average scores of public courses, career personality types, and library visits of previous graduates who chose different career paths.

[0075] Among them, previous graduates can choose from graduates of the previous three years or graduates of the previous five years;

[0076] S22 uses four grades: Excellent, Good, Pass, and Fail to classify the weighted average grades of professional courses and general courses, and uses four grades: Excellent, Good, and Average to classify the frequency of library visits, as follows:

[0077] The grading of the weighted average score for professional courses is as follows:

[0078] Weighted average score of professional courses Level Classification 85—100 excellent 70—84 good 60—69 Pass Less than 60 Fail

[0079] The grade classification for the weighted average score of public courses is as follows:

[0080]

[0081]

[0082] The library access frequency is categorized as follows:

[0083] Number of times entering and leaving the library Level Classification Greater than 600 excellent 300—600 good Less than 300 generally

[0084] S23. Based on the weighted average scores of professional courses, weighted average scores of general courses, career personality types, and library visit frequency data of previous graduates who chose different employment directions, determine the grade range DJa of the weighted average score of professional courses for each employment direction with the largest number of previous graduates. j And the number of previous graduates in that range, Na j Weighted average grade range for public courses (DJb) j and the number of previous graduates Nb in this range j DJc's Occupational Personality Type j And the number of previous graduates of this occupational personality type Nc j Library visit frequency range DJd j and the number of previous graduates in this range Nd j ;

[0085] S24. Based on the data collected in steps S1 and S23, calculate the employment success coefficient (JYCG) for recent graduates. j The calculation formula is as follows:

[0086]

[0087] It should be noted that when a i At or above DJa j When the interval value is , f(a) i DJA j ) equals 1, when a i Below DJa j When the interval value is , f(a) i DJA j ) equals 0;

[0088] It should be noted that when b i At or above DJb j When the interval value is , f(b) i DJb j When b equals 1, i Below DJb j When the interval value is , f(b) i DJb j ) equals 0;

[0089] It should be noted that when c i and DJc j When the occupational personality types are the same, f(c i DJc j ) equals 1, when c i and DJc j When occupational personality types are different, f(c) i DJc j) equals 0;

[0090] It should be noted that when d i At or above DJd j When the interval value is , f(d) i DJd j ) equals 1, when d i Below DJd j When the interval value is , f(d) i DJd j ) equals 0;

[0091] It should be noted that λ1, λ2, λ3, and λ4 are all preset proportionality coefficients greater than 0, and λ1+λ2+λ3+λ4=1;

[0092] It should be noted that, for the j-th employment direction, the weighted average score of the fresh graduates' professional courses, a i Weighted average score of public courses (b) i Occupational personality type c i Number of times entering and exiting the library (d) i The more similar to or even better the data on the weighted average grades of professional courses, weighted average grades of general courses, career personality type, and library visits of most previous graduates who chose this career path, the higher the success rate of this graduating student in choosing this career path. The success coefficient JYCG for this graduating student regarding the j-th career path is... j The larger it gets, the bigger it becomes.

[0093] S3: Collect satisfaction data from previous graduates who selected different employment directions, and combine this data with the data collected in steps S1 and S2 to calculate the employment direction satisfaction coefficient JYMY for current graduates. j It includes the following steps:

[0094] S31. Collect satisfaction data of previous graduates for different employment directions, and determine the average satisfaction of previous graduates for each employment direction. And determine that among the various employment directions, a i The number of previous graduates Na' in the weighted average grade range of their major courses j And the average satisfaction score of previous graduates in this range, MYa j b i The number of previous graduates Nb' in the weighted average grade range of their general education courses j And the average satisfaction score of previous graduates in this range, MYb j c i The number of previous graduates belonging to the corresponding occupational personality type Nc' j And the average satisfaction score (MYc) of previous graduates of this personality type.j d i The number of previous graduates in the library's visit frequency range Nd' j And the average satisfaction score of previous graduates in this range (MYd) j ;

[0095] It should be noted that the satisfaction level of previous graduates can be evaluated using a score scale of 1-10, where 1 represents the worst and 10 represents the highest. The satisfaction level of previous graduates can be obtained by sending a survey on employment direction satisfaction to previous graduates via email. The average satisfaction level of previous graduates within a category interval is the sum of the satisfaction levels of previous graduates within that category interval divided by the number of previous graduates within that category interval. The specific data can be obtained using data processing software.

[0096] S32, based on the data from step S31 and N from step S2 j Calculate the job satisfaction coefficient of recent graduates JYMY j The calculation formula is as follows:

[0097]

[0098] It should be noted that θ1, θ2, θ3, θ4, and θ5 are all preset proportionality coefficients greater than 0, and θ1+θ2+θ3+θ4+θ5=1;

[0099] It should be noted that, for the j-th employment direction, the a of the recent graduates i The average satisfaction rate of previous graduates within the weighted average grade range of their major courses (MYa) j b i The average satisfaction score of previous graduates within the weighted average grade range of the relevant general education courses (MYb) j c i Average satisfaction rate of previous graduates in the corresponding occupational personality type (MYc) j d i The average satisfaction level of previous graduates in the library's visit frequency range (MYd) j and the average satisfaction level of previous graduates. The larger the value, the more suitable this career path is for the recent graduate. The satisfaction coefficient JYMY for the j-th career path indicates this graduate's satisfaction with that career path. j The larger it gets, the bigger it becomes.

[0100] S4, based on the success rate of employment direction JYCG j and the job satisfaction coefficient JYMY j Generate employment direction prediction coefficient JYYC j The calculation formula is as follows:

[0101]

[0102] It should be noted that ω1, ω2, f1, and f2 are all preset proportionality coefficients greater than 0, and 0 < ω1 + ω2 < 1, and f1 + f2 = 1;

[0103] It should be noted that the success rate of employment direction is JYCG j and the job satisfaction coefficient JYMY j The larger the value, the better the future prospects for graduates choosing the j-th career path. The career path prediction coefficient JYYC j The larger it gets, the bigger it becomes.

[0104] S5, the employment direction prediction coefficient JYYC j The results are compared with the employment direction evaluation threshold TTH. If JYYC is met, the result is considered satisfactory. j If the employment direction prediction coefficient is ≥TTH, then the employment direction prediction coefficient JYYC will be used. j The corresponding employment direction will be recommended to the graduate as a career path to provide reference for graduating students. Conversely, if the JYYC requirement is not met, the graduate will be advised that the corresponding career path will be recommended. j If the employment direction prediction coefficient is ≥TTH, then the employment direction prediction coefficient JYYC will not be included. j The corresponding employment direction will be recommended to the graduate.

[0105] Example 2:

[0106] Please see Figure 2 The present invention provides an embodiment of a device for predicting the employment direction of college graduates, used in the above-mentioned method for predicting the employment direction of college graduates, comprising:

[0107] Data collection unit for recent graduates: used to collect the weighted average score of professional courses for recent graduates. i Weighted average score of public courses (b) i Occupational personality type c i Number of times entering and exiting the library (d) i Where i represents the ID of different graduating students, i = 1, 2, 3, ..., n;

[0108] Employment Direction Success Coefficient Calculation Unit: Used to collect the number N of previous graduates who have found employment after choosing different employment directions. j The data includes the weighted average scores of professional courses, the weighted average scores of general courses, career personality types, and library visits of previous graduates who chose different employment directions. Combined with the data collected in step S1, the employment success coefficient (JYCG) of the current year's graduates is calculated. j , where j represents the number of different employment directions, j = 1, 2, 3, ..., m;

[0109] Job Direction Satisfaction Coefficient Calculation Unit: This unit collects satisfaction data from previous graduates who selected different job directions, and combines this data with the data collected in steps S1 and S2 to calculate the job direction satisfaction coefficient JYMY for current graduates. j ;

[0110] Employment Direction Prediction Coefficient Calculation Unit: Used to calculate the employment direction success coefficient JYCG j and the job satisfaction coefficient JYMY j Generate employment direction prediction coefficient JYYC j ;

[0111] Recommended Employment Direction Generation Unit: Used to generate employment direction prediction coefficients JYYC j The results are compared with the employment direction evaluation threshold TTH. If JYYC is met, the result is considered satisfactory. j If the employment direction prediction coefficient is ≥TTH, then the employment direction prediction coefficient JYYC will be used. j The corresponding employment direction will be recommended to the graduate.

[0112] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0113] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by software, electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0114] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0115] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for predicting the employment direction of college graduates, characterized in that, Includes the following steps: S1, collect the weighted average score of the major courses of the graduating class. i Weighted average score of public courses (b) i Occupational personality type c i Number of times entering and exiting the library (d) i Where i represents the ID of different graduating students, i = 1, 2, 3, ..., n; S2, collect the number N of previous graduates who have found employment by selecting different employment directions. j The data includes the weighted average scores of professional courses, the weighted average scores of general courses, career personality types, and library visits of previous graduates who chose different employment directions. Combined with the data collected in step S1, the employment success coefficient (JYCG) of the current year's graduates is calculated. j , where j represents the number of different employment directions, j = 1, 2, 3, ..., m; S3: Collect satisfaction data from previous graduates who selected different employment directions, and combine this data with the data collected in steps S1 and S2 to calculate the employment direction satisfaction coefficient JYMY for current graduates. j ; S4, based on the success rate of employment direction JYCG j and the job satisfaction index JYMY j Generate employment direction prediction coefficient JYYC j ; S5, the employment direction prediction coefficient JYYC j The results are compared with the employment direction evaluation threshold TTH. If JYYC is met, the result is considered satisfactory. j If the employment direction prediction coefficient is ≥TTH, then the employment direction prediction coefficient JYYC will be used. j The corresponding career path will be recommended to the graduate.

2. The method for predicting the employment direction of college graduates according to claim 1, characterized in that: The occupational personality types are divided into four categories: extraversion, agreeableness, stability, and openness.

3. The method for predicting the employment direction of college graduates according to claim 1, characterized in that: The number N of previous graduates who selected different employment directions was collected. j The data includes the weighted average scores of professional courses, the weighted average scores of general courses, career personality types, and library visits of previous graduates who chose different employment directions. Combined with the data collected in step S1, the employment success coefficient (JYCG) of the current year's graduates is calculated. j Where j represents the number of different employment directions, j = 1, 2, 3, ..., m, the specific logic is as follows: S21, Collect the number N of previous graduates who chose different employment directions. j The data also includes the weighted average scores of professional courses, the weighted average scores of public courses, career personality types, and library visits of previous graduates who chose different career paths. S22 uses the grades of weighted average scores of professional courses and weighted average scores of public courses to classify them as excellent, good, pass, and fail, and uses the grades of library visits to classify them as excellent, good, and average. S23. Based on the weighted average scores of professional courses, weighted average scores of general courses, career personality types, and library visit frequency data of previous graduates who chose different employment directions, determine the grade range DJa of the weighted average score of professional courses for each employment direction with the largest number of previous graduates. j And the number of previous graduates in that range, Na j Weighted average grade range for public courses (DJb) j and the number of previous graduates Nb in this range j DJc's Occupational Personality Type j And the number of previous graduates of this occupational personality type Nc j Library visit frequency range DJd j and the number of previous graduates in this range Nd j ; S24. Based on the data collected in steps S1 and S23, calculate the employment success coefficient (JYCG) for recent graduates. j The calculation formula is as follows: Where, when a i At or above DJa j When the interval value is , f(a) i DJA j ) equals 1, when a i Below DJa j When the interval value is , f(a) i DJA j ) equals 0; Among them, when b i At or above DJb j When the interval value is , f(b) i DJb j ) equals 1, when b i Below DJb j When the interval value is , f(b) i DJb j ) equals 0; Where, when c i and DJc j When the occupational personality types are the same, f(c i DJc j ) equals 1, when c i and DJc j When occupational personality types are different, f(c) i DJc j ) equals 0; Where, when d i At or above DJd j When the interval value is , f(d) i DJd j ) equals 1, when d i Below DJd j When the interval value is , f(d) i DJd j ) equals 0; Wherein, λ1, λ2, λ3, and λ4 are all preset proportional coefficients greater than 0, and λ1+λ2+λ3+λ4=1.

4. The method for predicting the employment direction of college graduates according to claim 3, characterized in that: The employment options are categorized as civil servant, public institution employee, state-owned enterprise, private enterprise, foreign enterprise, and further education.

5. The method for predicting the employment direction of college graduates according to claim 3, characterized in that: The specific logic behind classifying the weighted average grades of professional courses and general courses into excellent, good, pass, and fail categories, and classifying the frequency of library visits into excellent, good, and average categories, is as follows: Firstly, the grading of the weighted average score for professional courses is as follows: When the weighted average score of professional courses is between 85 and 100, it is classified as excellent. When the weighted average score of professional courses is between 70 and 84, it is classified as a good grade. When the weighted average score of professional courses is between 60 and 69, it is classified as a passing grade. When the weighted average score of professional courses is below 60, it is classified as failing. Secondly, the grade classification for the weighted average score of public courses is as follows: When the weighted average score of public courses is between 85 and 100, it is classified as excellent. When the weighted average score of public courses is between 70 and 84, it is classified as a good grade. When the weighted average score of public courses is between 60 and 69, it is classified as a passing grade. When the weighted average score of public courses is below 60, it is classified as failing. Thirdly, the library's access frequency is categorized as follows: If a user enters or exits the library more than 600 times, they are classified as an excellent user. When the number of visits to the library is between 300 and 600, it is classified as a good range; If a user enters or exits the library less than 300 times, the user is classified as belonging to the general range.

6. The method for predicting the employment direction of college graduates according to claim 1, characterized in that: The process involves collecting satisfaction data from previous graduates across different career paths, and combining this data with that collected in steps S1 and S2 to calculate the employment direction satisfaction coefficient JYMY for current graduates. j The specific logic is as follows: S31. Collect satisfaction data of previous graduates for different employment directions, and determine the average satisfaction of previous graduates for each employment direction. And determine that among the various employment directions, a i The number of previous graduates Na' in the weighted average grade range of their major courses j And the average satisfaction score of previous graduates in this range, MYa j b i The number of previous graduates Nb' in the weighted average grade range of their general education courses j And the average satisfaction score of previous graduates in this range, MYb j c i The number of previous graduates belonging to the corresponding occupational personality type Nc' j And the average satisfaction score (MYc) of previous graduates of this personality type. j d i The number of previous graduates in the library's visit frequency range Nd' j And the average satisfaction score of previous graduates in this range (MYd) j ; S32, based on the data from step S31 and N from step S2 j Calculate the job satisfaction coefficient of recent graduates JYMY j The calculation formula is as follows: Among them, θ1, θ2, θ3, θ4, and θ5 are all preset proportional coefficients greater than 0, and θ1+θ2+θ3+θ4+θ5=1.

7. The method for predicting the employment direction of college graduates according to claim 6, characterized in that: The satisfaction level of previous graduates can be evaluated using a score scale of 1-10, where 1 represents the worst and 10 represents the best.

8. The method for predicting the employment direction of college graduates according to claim 1, characterized in that: The employment direction prediction coefficient JYYC j The calculation formula is as follows: Wherein, ω1, ω2, f1, and f2 are all preset proportional coefficients greater than 0, and 0 < ω1 + ω2 < 1, and f1 + f2 = 1.

9. A device for predicting the employment direction of college graduates, used in the method for predicting the employment direction of college graduates as described in any one of claims 1-8, characterized in that, include: Data collection unit for recent graduates: used to collect the weighted average score of professional courses for recent graduates. i Weighted average score of public courses (b) i Occupational personality type c i Number of times entering and exiting the library (d) i Where i represents the ID of different graduating students, i = 1, 2, 3, ..., n; Employment Direction Success Coefficient Calculation Unit: Used to collect the number N of previous graduates who have found employment after choosing different employment directions. j The data includes the weighted average scores of professional courses, the weighted average scores of general courses, career personality types, and library visits of previous graduates who chose different employment directions. Combined with the data collected in step S1, the employment success coefficient (JYCG) of the current year's graduates is calculated. j , where j represents the number of different employment directions, j = 1, 2, 3, ..., m; Employment Direction Satisfaction Coefficient Calculation Unit: This unit collects satisfaction data from previous graduates who selected different employment directions, and combines this data with the data collected in steps S1 and S2 to calculate the employment direction satisfaction coefficient (JYMY) for current graduates. j ; Employment Direction Prediction Coefficient Calculation Unit: Used to calculate the employment direction success coefficient JYCG j and the job satisfaction index JYMY j Generate employment direction prediction coefficient JYYC j ; Recommended Employment Direction Generation Unit: Used to generate employment direction prediction coefficients JYYC j The results are compared with the employment direction evaluation threshold TTH. If JYYC is met, the result is considered satisfactory. j If the employment direction prediction coefficient is ≥TTH, then the employment direction prediction coefficient JYYC will be used. j The corresponding career path will be recommended to the graduate.

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