An employment counseling system based on artificial intelligence AI

Abstract

The application relates to the field of artificial intelligence technology, in particular to an employment guidance system based on artificial intelligence AI, which comprises a database module, an analysis module, an information receiving module and a verification module. The database module can store enterprise sample data information of different professional types and different employment stages of different education stages in a historical period, so that enterprise matching characteristic factors can be determined, different employment target body individual difference characteristics can be found through analysis, and employment recommendation accuracy is improved. The verification module can divide employment guidance labels, mine potential conditions of matching enterprises of a user end, consider online enterprise categories with potential tendencies that have not been browsed by the user end based on online enterprise pages accessed by the user end, match potential enterprise tendencies of the user end, effectively push online enterprises based on each accessed online enterprise category, and further improve the efficiency of employment matching enterprise pushing.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of artificial intelligence, and particularly relates to an employment guidance system based on artificial intelligence AI. BACKGROUND

[0002] In today's digital age, the employment guidance system based on artificial intelligence AI is booming with the support of a series of cutting-edge technologies. Machine learning technology is its core support. Through learning from massive employment data, the system can mine the potential connection between job seekers and positions, accurately screen resumes and recommend matching positions based on past successful cases. Natural language processing technology enables the system to understand and process recruitment information and resume texts, extract key skills, job requirements and other content, and help intelligent matching. It can also drive intelligent chat robots to provide real-time consultation. Big data technology provides rich data sources for the system, covering recruitment websites, social media and other multi-channel data. Through analysis and insight into the employment market trends and changes in talent supply and demand, the system can recommend suitable positions, learning resources and career development suggestions for job seekers based on their characteristics and behavior data, and recommend qualified candidates for enterprises.

[0003] Chinese Patent Publication No. CN117408564B discloses an online academic guidance system, which includes a biological data collection module, a learning plan development module, a facial analysis module, and a learning plan adjustment module. The biological data collection module is used to collect biological rhythm data and facial expression data of users. The learning plan development module includes a biological rhythm analysis module and a learning plan generation module. The biological rhythm analysis module applies machine learning algorithms to analyze the collected biological rhythm data and determines the current learning efficiency of the user based on the biological rhythm data. The learning plan generation module adjusts the user's learning plan based on the current learning efficiency determined by the data analysis module to improve and optimize the learning efficiency.

[0004] The Chinese patent publication No. CN117436830B discloses a graduate employment enterprise identification system, which comprises: obtaining a low evaluation parameter according to the ratio of the number of likes to the number of comments in each grouping data of the enterprise, obtaining the weight of the low evaluation parameter of the grouping data according to the difference between the mean value of the grouping comment quantity and the like quantity and the difference of each grouping comment quantity and like quantity combined with the grouping sequence and the neighborhood grouping difference, weighting the low evaluation parameter with the low evaluation parameter weight to obtain the difference evaluation degree, dividing the enterprise grouping data into two parts according to the time sequence and calculating the expected parameter, and comprehensively judging the enterprise recommendation degree combined with the expected parameter and the difference evaluation degree. The present application can more accurately judge whether the enterprise is excellent, so as to enable the job seeker to more efficiently and accurately select the excellent enterprise and reduce the enterprise matching.

[0005] However, the prior art still has the following problems:

[0006] The prior art does not consider that the different professional types and different education stage characteristics of the user end lead to different enterprise tendency categories of the user end, and the balanced push of big data to different user ends for employment guidance causes the enterprise upload frequency of the user end to be unsuitable, resulting in low efficiency of enterprise employment guidance. SUMMARY

[0007] Therefore, the present application provides an employment guidance system based on artificial intelligence AI to overcome the problem that the prior art does not consider that the different professional types and different education stage characteristics of the user end lead to different enterprise tendency categories of the user end, and the balanced push of big data to different user ends for employment guidance causes the enterprise upload frequency of the user end to be unsuitable, resulting in low efficiency of enterprise employment guidance.

[0008] To achieve the above purpose, the present application provides an employment guidance system based on artificial intelligence AI, which comprises:

[0009] A database module is used to store the enterprise sample data information of different professional types and different education stages extracted in advance in a historical period, and to determine the enterprise matching characteristic factors of different professional types and different education stages of the target body;

[0010] An analysis module is connected with the database module, and is used to calculate the enterprise matching characteristic parameters of different professional types and different education stages of the target body according to the enterprise matching characteristic factors of different professional types and different education stages of the target body;

[0011] An information receiving module is connected with the user end, and is used to obtain the actual data of the professional type of the target body uploaded by the user end and the enterprise sample data information of the corresponding education stage;

[0012] a verification module connected with the database module, the analysis module and the information receiving module respectively, configured to obtain enterprise matching characteristic parameters of the target body of each education stage of the corresponding professional type uploaded by the user end according to the enterprise sample data information of the enterprise sample data information, and to divide the employment guidance label according to the enterprise matching characteristic parameters;

[0013] configured to push the actual data of the target body uploaded by the user end to the enterprise verification push based on the division result of the employment guidance label, including,

[0014] configured to obtain the response of the click behavior of the user end after the push verification push, to determine whether to adjust the enterprise push mode, and to push the enterprise according to the push mode; or, to collect the number of click behaviors of the user end page to determine to increase the push frequency.

[0015] Further, the database module is configured to determine the enterprise matching characteristic factors of the target body of each education stage of different professional types, including,

[0016] comparing the enterprise sample data information of each education stage of a single professional type with the enterprise sample data information of the corresponding education stage of the target body of each professional type;

[0017] solving the database employment rate average, and obtaining the employment difference degree of the target body of each education stage of a single professional type.

[0018] Further, the database module is configured to obtain the employment difference degree of the target body of each education stage of a single professional type, including,

[0019] calibrating the number of employment types and the total number of employment positions of the education stage of the target body of a single professional type;

[0020] calculating the ratio of the number of employment types of the target body in a single professional type to the benchmark employment type threshold value to determine the first difference influence factor;

[0021] calculating the ratio of the total number of employment positions of the target body in a single professional type to the benchmark total number of employment positions threshold value to determine the second difference influence factor;

[0022] determining the sum of the first difference influence factor and the second difference influence factor as the employment difference degree.

[0023] Further, the analysis module is configured to calculate the enterprise matching characteristic parameters of the target body of different professional types of different education stages, including,

[0024] extracting the database employment rate average and the employment difference degree;

[0025] determining a ratio of the database employment rate average to a benchmark database employment rate threshold as a first matching characteristic factor;

[0026] determining a ratio of the employment difference degree to a benchmark employment difference degree threshold as a second matching characteristic factor;

[0027] determining a sum of the first matching characteristic factor and the second matching characteristic factor as an enterprise matching characteristic parameter.

[0028] Further, the verification module is used to divide an employment guidance label according to the enterprise matching characteristic parameter, including,

[0029] if the enterprise matching characteristic parameter is less than an enterprise matching characteristic threshold, determining a first label category;

[0030] if the enterprise matching characteristic parameter is greater than or equal to the enterprise matching characteristic threshold, determining a second label category.

[0031] Further, the verification module is used to push an enterprise verification based on a division result of the employment guidance label, including,

[0032] if the first label category, performing the enterprise verification push, obtaining a response of each feature after the enterprise verification push, and determining whether to adjust the enterprise push mode to push the enterprise according to the push mode and then perform the verification again;

[0033] if the second label category, collecting a number of times of click behaviors of a user terminal page to determine whether to increase a push frequency and then perform the verification again.

[0034] Further, the verification module is used to obtain a response of a click behavior of the user terminal after the enterprise verification push to determine whether to adjust the enterprise push mode, including,

[0035] extracting a reduction amount of the number of times of the click behavior of the user terminal in a time period;

[0036] if the reduction amount of the number of times of the click behavior of the user terminal is greater than or equal to a predetermined change threshold, determining to adjust the push mode to push the enterprise according to the push mode.

[0037] Further, the verification module is used to adjust the enterprise push mode to push the enterprise according to the push mode, including,

[0038] determining keywords in an online enterprise page of the employment guidance system;

[0039] determining an online enterprise category to which each of the keywords belongs;

[0040] wherein, a belonging relationship between each of the keywords and the online enterprise category is preset.

[0041] determining remaining unvisited online enterprise categories based on the visited online enterprise categories;

[0042] determining re-verification after online enterprise push for each of the unvisited online enterprise categories.

[0043] Further, the verification module is used to collect the number of click behaviors of the user terminal page to determine the increase of the push frequency, including,

[0044] extracting the number of page clicks of the user terminal before the online enterprise behavior;

[0045] If the number of page clicks is greater than or equal to a predetermined page click threshold, it is determined that the user terminal appears enterprise matching behavior, and the push frequency is increased to re-verify.

[0046] Further, the verification module is used to increase the push frequency, including,

[0047] collecting the average of the decreasing amount of the number of click behaviors of the user terminal within a time period;

[0048] determining that the increase of the online enterprise push frequency is positively correlated with the average of the decreasing amount.

[0049] Compared with the prior art, the application provides an employment guidance system based on artificial intelligence AI, which comprises a database module, an analysis module, an information receiving module and a verification module. The database module can store enterprise sample data information of different professional types and different employment stages of different education stages in the historical period, so as to determine the enterprise matching characteristic factors of different professional types and different education stages of the target body. The analysis module can analyze the enterprise matching characteristic parameters of different professional types and different education stages of the target body according to the enterprise matching characteristic factors of different professional types and different education stages of the target body, find the individualized difference characteristics of each professional type and each education stage, and improve the accuracy of analysis. The verification module can obtain the enterprise matching characteristic parameters of each education stage of the corresponding professional type according to the enterprise sample data information, divide different employment guidance label types according to the enterprise matching characteristic parameters, mine the potential matching tendency of the user end, and push the actual data of the target body uploaded by the user end to the enterprise verification push based on the division result of the employment guidance label. By obtaining the response of the user end click behavior after the push verification push, it can be determined whether to adjust the enterprise push mode. Therefore, the application considers the online enterprise page accessed by the user end, considers the online enterprise category with potential tendency that has not been browsed by the user end, matches the potential matching tendency of the user end, determines the online enterprise page accessed by the user end based on the online enterprise page, determines the online enterprise category accessed based on the online enterprise page, and effectively adjusts the enterprise push mode of the online enterprise based on each accessed online enterprise category, thereby further improving the efficiency and accuracy of the employment matching enterprise push of the system.

[0050] Especially, by obtaining the enterprise matching characteristic factors and the enterprise matching characteristic parameters of the user end of different professional types and different education stages before the employment application behavior, the employment guidance label tendency can be divided, the enterprise verification push can be pushed based on the division result, the online enterprise push mode and the push frequency of each user end can be customized, the enterprise matching characteristics of each user end can be analyzed based on the change of the user end click behavior, the efficiency of the online enterprise matching intelligent recommendation of the system is improved, the misjudgment of the user end to the online enterprise push is reduced, the online enterprise page accessed by the user end is determined, the accessed online enterprise category is determined based on each online enterprise page, and the enterprise push method and the push frequency of each online enterprise category can be effectively adjusted based on each accessed online enterprise category, thereby further improving the efficiency of the employment guidance enterprise push of the system.

[0051] Especially, for the second label category of the employment guidance label, the number of click behaviors of a user terminal page is collected, whether the user terminal matches the recommended enterprise is analyzed based on the number of click behaviors, verification is performed again after increasing the push frequency, the occurrence of an abnormal enterprise matching condition of the user terminal is reduced, and the employment guidance enterprise push efficiency of the system is improved. BRIEF DESCRIPTION OF DRAWINGS

[0052] Figure 1 A structure block diagram of an employment guidance system based on artificial intelligence AI of an embodiment of the present application;

[0053] Figure 2 A step flowchart for determining enterprise matching characteristic factors of target bodies of different professional types and different education stages in employment of an embodiment of the present application;

[0054] Figure 3 A step flowchart for obtaining employment difference degrees of target bodies of a single professional type in different education stages of employment of an embodiment of the present application;

[0055] Figure 4 A step flowchart for calculating enterprise matching characteristic parameters of target bodies of different professional types in different education stages of employment of an embodiment of the present application. DETAILED DESCRIPTION

[0056] In order to make the objects and advantages of the present application clearer, the present application will be further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present application, and do not limit the present application.

[0057] The preferred embodiments of the present application will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present application, and are not intended to limit the protection scope of the present application.

[0058] It should be noted that, in the description of the present application, unless otherwise explicitly specified and limited, the term "connected" should be understood broadly, for example, it can be fixedly connected, or detachably connected, or integrally connected; it can be mechanically connected, or electrically connected; it can be directly connected, or indirectly connected through an intermediate medium, or connected inside two elements. Those skilled in the art can understand the specific meaning of the above-mentioned term in the present application according to the specific circumstances.

[0059] Please refer to Figure 1 shown, which is a structure block diagram of an employment guidance system based on artificial intelligence AI of an embodiment of the present application, the present application provides an employment guidance system based on artificial intelligence AI, which comprises:

[0060] a database module configured to store enterprise sample data information of different professional types and different education stages extracted in advance in a historical period, so as to determine enterprise matching characteristic factors of the target body of different professional types and different education stages in each education stage;

[0061] an analysis module connected with the database module, configured to calculate enterprise matching characteristic parameters of the target body of different professional types and different education stages according to the enterprise matching characteristic factors of the target body of different professional types in each education stage;

[0062] an information receiving module connected with the user end, configured to obtain actual data of the target body of the professional type and enterprise sample data information of the corresponding education stage uploaded by the user end;

[0063] a verification module connected with the database module, the analysis module and the information receiving module respectively, configured to obtain enterprise matching characteristic parameters of each education stage of the corresponding professional type of the target body uploaded by the user end according to the enterprise sample data information, and divide an employment guidance label according to the enterprise matching characteristic parameters;

[0064] configured to push an enterprise verification to the actual data of the target body uploaded by the user end based on the division result of the employment guidance label, including,

[0065] configured to obtain a response condition of a click behavior of the user end after the push verification is pushed, so as to determine whether to adjust an enterprise push mode to push the enterprise according to the push mode; or, collect a number of click behaviors of the user end page, so as to determine to increase a push frequency.

[0066] In the implementation, first, the enterprise sample data information of different professional types and different education stages extracted in advance in a historical period is stored, so as to determine enterprise matching characteristic factors of the target body of different professional types and different education stages in each education stage; then, enterprise matching characteristic parameters of the target body of different professional types and different education stages are calculated according to the enterprise matching characteristic factors of the target body of different professional types in each education stage; finally, actual data of the target body of the professional type and enterprise sample data information of the corresponding education stage uploaded by the user end are obtained; enterprise matching characteristic parameters of each education stage of the corresponding professional type of the target body uploaded by the user end are obtained according to the enterprise sample data information, and an employment guidance label is divided according to the enterprise matching characteristic parameters; the actual data of the target body uploaded by the user end is pushed to an enterprise verification based on the division result of the employment guidance label, if it is a first label category, the response condition of the click behavior of the user end after the push verification is pushed is obtained, so as to determine whether to adjust the enterprise push mode to push the enterprise according to the push mode; if it is a second label category, the number of click behaviors of the user end page is collected, so as to determine to increase the push frequency.

[0067] Specifically, for the enterprise sample data information is pre-stored construction of each professional type of each stage of employment of the corresponding big data model of enterprise, but also other forms, this will not be repeated.

[0068] Specifically, the division of professional categories is not limited, in the implementation, the professional categories are mathematics, physics, chemistry, biology, Chinese, English, and other forms can also be used, this will not be repeated.

[0069] Specificly, the division of education stage is not limited, in the implementation, the education stage is preschool education stage, nine-year compulsory education stage, high school education stage, college education stage, undergraduate education stage and postgraduate education stage, other forms can also be used, this will not be repeated.

[0070] Specifically, the specific structure of the database module, the analysis module, the information receiving module and the verification module is not limited, each unit in it can be composed of a logic component, which includes a field programmable component, a computer or a microprocessor in a computer.

[0071] Specifically, the historical period is not limited, in the implementation, it is the professional type of each stage of employment of the enterprise sample data information model stored and analyzed in the previous three months of the system.

[0072] Specifically, the way to obtain the user's behavior click behavior characteristics is not limited, related tracking software can be used to obtain, for example, one of ClickMagick, Trackier, of course, other forms can also be used, as long as the number of click behavior can be obtained.

[0073] Specifically, the present application provides an employment guidance system based on artificial intelligence AI, comprising a database module, an analysis module, an information receiving module and a verification module. The analysis module can analyze the enterprise matching characteristic parameters of different professional types of target bodies in different education stages according to the enterprise matching characteristic factor of different professional types of target bodies in different education stages, find the individualized difference characteristics of different professional types and different education stages, and improve the accuracy of analysis. The verification module can obtain the enterprise matching characteristic parameters of different professional types in different education stages according to the enterprise sample data information, divide different employment guidance label types according to the enterprise matching characteristic parameters, mine the potential matching tendency of the user end, and push the actual data of the target body uploaded by the user end to the enterprise verification based on the division result of the employment guidance label. By obtaining the response of the user end after the push verification, it can be determined whether to adjust the enterprise push mode. Therefore, the present application considers the online enterprise page accessed by the user end, considers the online enterprise category with potential tendency that has not been browsed by the user end, matches the potential matching tendency of the user end, determines the online enterprise page accessed by the user end, determines the online enterprise category accessed based on the online enterprise page, and effectively targets the enterprise push mode of the online enterprise based on each accessed online enterprise category, thereby further improving the efficiency and accuracy of the employment matching enterprise push of the system.

[0074] Referring to Figure 2 The database module is used to determine the enterprise matching characteristic factor of the target body in different professional types in different education stages, and comprises,

[0075] The enterprise sample data information of each education stage of a single professional type is compared with the enterprise sample data information of the corresponding education stage of the target body of other professional types.

[0076] The database employment rate average is solved, and the employment difference degree of the target body of a single professional type in each education stage is obtained.

[0077] In the implementation, the database employment rate average is determined as the ratio of the number of employed persons registered on the platform to the total number of job seekers on the platform in the enterprise sample data information stored in the historical period system running within the last three months, which will not be repeated here.

[0078] Referring to Figure 3 The present application obtains the employment difference degree of the target body of a single professional type in different education stages, and the database module comprises,

[0079] the number of employment categories of the target body in the single professional type and the total number of employment positions;

[0080] a ratio of the number of employment categories of the target body in the single professional type to a threshold value of the benchmark employment categories is determined as a first difference influence factor;

[0081] a ratio of the total number of employment positions of the target body in the single professional type to a threshold value of the benchmark total number of employment positions is determined as a second difference influence factor;

[0082] a sum of the first difference influence factor and the second difference influence factor is determined as an employment difference degree.

[0083] In implementation, the threshold value of the benchmark employment categories is predetermined, wherein an average value of the number of employment categories of the target body in the professional type of the computing platform in a historical period is pre-acquired, the threshold value is set as a product of the average value of the number of employment categories and a precision coefficient, the precision system is selected in an interval [1.1, 1.2], and preferably, the historical period is three months.

[0084] Similarly, the threshold value of the benchmark total number of employment positions is derived from a product of the total number of employment positions corresponding to the historical period of big data and a precision coefficient, which is not repeated here.

[0085] The difference degree is a sum of values of the first difference influence factor and the second difference influence factor, which is not repeated here.

[0086] Please refer to Figure 4 Fig. 1 shows a step flow chart for calculating enterprise matching characteristic parameters of different professional types of target bodies in different education stages according to an embodiment of the present application, and the analysis module for calculating enterprise matching characteristic parameters of different professional types of target bodies in different education stages comprises,

[0087] extracting a database employment rate average value and an employment difference degree;

[0088] determining a ratio of the database employment rate average value to a threshold value of the benchmark database employment rate as a first matching characteristic factor;

[0089] determining a ratio of the employment difference degree to a threshold value of the benchmark employment difference degree as a second matching characteristic factor;

[0090] determining a sum of the first matching characteristic factor and the second matching characteristic factor as the enterprise matching characteristic parameter.

[0091] Specifically, the threshold value of the benchmark database employment rate is 1.15 times of an average value of the database employment rate in the system in the historical period; similarly, the threshold value of the benchmark employment difference degree is 1.2 times of an average value of the employment difference degree in the three months before the system recommendation behavior in the historical period

[0092] Specifically, the verification module is configured to divide the employment guidance label according to the enterprise matching characteristic parameter, including,

[0093] If the enterprise matching characteristic parameter is less than the enterprise matching characteristic threshold, it is determined as the first label category;

[0094] If the enterprise matching characteristic parameter is greater than or equal to the enterprise matching characteristic threshold, it is determined as the second label category.

[0095] Specifically, the enterprise matching characteristic threshold is obtained in advance, which is 1.12 times the average value of the database enterprise matching characteristic parameter in the first three months before the historical period system runs.

[0096] Specifically, the verification module is configured to push the actual data of the target body uploaded by the user end to the enterprise verification push based on the division result of the employment guidance label, including,

[0097] If it is the first label category, the push verification push is performed, the response of each feature after the push verification push is obtained, and whether to adjust the enterprise push mode is determined to re-verify after the corresponding push mode is pushed to the enterprise;

[0098] If it is the second label category, the number of click behaviors of the user end page is collected to determine whether to re-verify after increasing the push frequency.

[0099] In implementation, in response to the division result of the employment guidance label, the online enterprise category required by the user end for employment enterprise recommendation is determined, the online enterprise page corresponding to the professional type corresponding to the education stage characteristic of the user end is determined, the online enterprise category to which each keyword belongs is determined by the keyword of the online enterprise page, the enterprise corresponding to the online enterprise category is recommended, and then whether to adjust the push mode is determined by analyzing the change of the number of click behaviors of the user end after the online enterprise recommendation.

[0100] Specifically, the process of the verification module for obtaining the response of the user end click behavior after the push verification push to determine whether to adjust the enterprise push mode includes,

[0101] Extracting the reduction amount of the number of user end click behaviors in the time period;

[0102] If the reduction amount of the number of user end click behaviors is greater than or equal to the predetermined change threshold, it is determined to adjust the push mode to push the enterprise corresponding to the push mode.

[0103] In implementation, the change threshold of the number of click behaviors is determined based on the corresponding click behavior number threshold, which is set to 1.1 to 1.2 times the click behavior number threshold.

[0104] Specifically, the verification module is used to adjust the enterprise pushing mode to push the enterprise according to the pushing mode, including,

[0105] Determine the keywords in the online enterprise page of the employment guidance system;

[0106] Determine the online enterprise category to which each keyword belongs;

[0107] Wherein, the attribution relationship between each keyword and online enterprise category is preset;

[0108] Based on the accessed online enterprise category, determine the remaining unvisited online enterprise category;

[0109] Determine the online enterprise pushing after re-verification for each unvisited online enterprise category.

[0110] Specifically, the verification module is used to increase the pushing frequency, including,

[0111] Collect the decreasing amount mean of the number of user terminal click behaviors in a time period;

[0112] Determine that the increasing amount of online enterprise pushing frequency is positively correlated with the decreasing amount mean.

[0113] In implementation, solve the ratio of the decreasing amount mean and the Euclidean change threshold value, multiply the obtained ratio value and the initial online enterprise pushing frequency to obtain the online enterprise pushing frequency that needs to be reduced.

[0114] Specifically, for the second label category of the employment guidance label, by collecting the number of user terminal page click behaviors, analyzing whether the user terminal matches the recommended enterprise based on the number of click behaviors, re-verification after increasing the pushing frequency, reduces the occurrence of user terminal enterprise matching exceptions, and improves the employment guidance enterprise pushing efficiency of the system.

[0115] So far, the technical solutions of the present application have been described in combination with the preferred embodiments shown in the drawings, but those skilled in the art can easily understand that the protection scope of the present application is obviously not limited to these specific embodiments. Without deviating from the principles of the present application, those skilled in the art can make equivalent changes or replacements to related technical features, and the technical solutions after these changes or replacements will fall within the protection scope of the present application.

Claims

1. An employment counseling system based on artificial intelligence (AI), characterized by: include: The database module is used to store sample data of enterprises with different professional types and different educational levels extracted in advance during the historical period, and is used to determine the matching characteristic factors of enterprises with different professional types and different educational levels employed by target subjects at different educational levels; An analysis module, connected to the database module, is used to calculate enterprise matching characteristic parameters for target persons of different professional types at different educational levels based on enterprise matching characteristic factors for target persons of different professional types at different educational levels; An information receiving module is connected to the user terminal and is used to obtain the actual data of the target subject's professional type and the enterprise sample data information corresponding to the educational level uploaded by the user terminal; a verification module, which is connected to the database module, the analysis module, and the information receiving module respectively, and is used to obtain enterprise matching feature parameters corresponding to the professional type and educational level of the target entity uploaded by the user terminal based on the enterprise sample data information, and to divide the employment counseling label according to the enterprise matching feature parameters; Based on the classification result of the employment counseling tag, the actual data of the target object uploaded by the user is put into the enterprise for verification and push, including: To obtain the response of the user's click behavior after the delivery verification push, so as to determine whether to adjust the enterprise push method and push the enterprise with the corresponding push method; Alternatively, collect the number of clicks on the user's page to determine how to increase the push frequency.

2. The employment counseling system based on artificial intelligence (AI) according to claim 1 is characterized in that: The database module is used to determine the enterprise matching characteristic factors of target subjects of different professional types and different educational levels at different educational levels, including: Compare the sample data of enterprises at each educational level of a single professional type with the sample data of enterprises at the corresponding educational level of the target population of other professional types; Solve the mean employment rate of the database and obtain the employment difference of each academic level of the target body of a single professional type.

3. The employment counseling system based on artificial intelligence (AI) according to claim 2 is characterized in that: The database module is used to obtain the employment differences of a single professional type target at different educational levels. include, Determine the number of employment types and total number of employment positions at the academic level for the target employment of a single professional type; The ratio of the number of employment types of the target body in a single professional type to the benchmark employment type threshold is calculated and determined as the first difference impact factor; The ratio of the total number of employment positions of the target body in a single professional type to the total number of benchmark employment positions is calculated as the second difference influencing factor; The sum of the first difference influencing factor and the second difference influencing factor is determined as the employment difference degree.

4. The employment counseling system based on artificial intelligence (AI) according to claim 3 is characterized in that: The analysis module is used to calculate the enterprise matching characteristic parameters for target persons of different professional types and different educational levels. include, Extract the mean employment rate and employment variability of the database; The ratio of the mean employment rate of the database to the employment rate threshold of the benchmark database is determined as the first matching feature factor; determining the ratio of the employment difference degree to the benchmark employment difference degree threshold as the second matching characteristic factor; The sum of the first matching feature factor and the second matching feature factor is determined as an enterprise matching feature parameter.

5. The employment counseling system based on artificial intelligence (AI) according to claim 4 is characterized in that: The verification module is used to divide the employment counseling labels according to the enterprise matching feature parameters. include, If the enterprise matching feature parameter is less than the enterprise matching feature threshold, it is determined to be the first label category; If the enterprise matching feature parameter is greater than or equal to the enterprise matching feature threshold, it is determined to be the second label category.

6. The employment counseling system based on artificial intelligence (AI) according to claim 5 is characterized in that: The verification module is used to perform enterprise verification push on the actual data of the target object uploaded by the user terminal based on the classification result of the employment guidance label, including: If it is the first tag category, then push the delivery verification, obtain the response of each feature after the delivery verification push, determine whether to adjust the enterprise push method, push the enterprise with the corresponding push method and then re-verify; If it is the second tag category, the number of clicks on the user's page is collected to determine whether to increase the push frequency and then re-verify.

7. The employment counseling system based on artificial intelligence (AI) according to claim 1, characterized in that: The verification module is used to obtain the response of the user's click behavior after the verification push is delivered to determine whether to adjust the enterprise push method. include, Extract the reduction in the number of clicks by the user during the time period; If the decrease in the number of click behaviors on the user side is greater than or equal to a predetermined change threshold, it is determined that the push method is adjusted and the enterprise is pushed in the corresponding push method.

8. The employment counseling system based on artificial intelligence (AI) according to claim 1 is characterized in that: The verification module is used to adjust the enterprise push mode and push the enterprise in the corresponding push mode, including: Identify keywords in the online company page of the employment counseling system; determining the online business category to which each of the keywords belongs; Among them, the attribution relationship between each keyword and the online enterprise category is preset; Based on the categories of online businesses that have been visited, determining the remaining categories of online businesses that have not been visited; After determining to push online enterprises for each of the online enterprise categories that have not been visited, re-verification is performed.

9. The employment counseling system based on artificial intelligence (AI) according to claim 1, characterized in that: The verification module is used to collect the number of clicks on the user's page to determine the increase in push frequency, including: Extract the number of page clicks by the user before the online enterprise behavior; If the number of page clicks is greater than or equal to a predetermined page click threshold, it is determined that enterprise matching behavior occurs on the user side, and the push frequency is increased and re-verification is performed.

10. The employment counseling system based on artificial intelligence (AI) according to claim 1, characterized in that: The process of the verification module for increasing the push frequency includes: The average decrease in the number of clicks by the user during the collection period; It is determined that the increase in the frequency of online enterprise push notifications is positively correlated with the mean of the decrease.

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