Design And Implementation Of Professional Diversion System Based On Deep Learning

With the rapid development of social economy,Chinese colleges and universities are gradually changing from major enrollment to class enrollment and distributary training mode.With the increase of college enrollment,the traditional artificial professional diversion is gradually eliminated,and the professional diversion system emerges as The Times require.Through the analysis of the current professional diversion mechanism and system,summed up the current professional diversion mode is facing two major problems.On the one hand,some colleges and universities give insufficient guidance to students,which leads to students blindly following the trend when choosing majors,resulting in one-sided choice of majors.On the other hand,after being adjusted by the major,students do not have a high sense of professional identity,resulting in a substantial decline in academic performance,resulting in students' self-abandonment,weariness and other bad emotions,wasting the educational resources of colleges and universities.In order to effectively solve the above problems,this paper designs and implements a professional diversion system based on deep learning.Compared with the traditional professional diversion system,this system has two innovations.On the one hand,deep learning technology is systematically applied to construct a classification model of students' professional fitness based on deep neural network,which is evaluated and compared with traditional machine learning classification algorithm,and it is found that the classification effect is significantly better than the latter.In this paper,this model is applied to the system to recommend students'suitable majors and give appropriate guidance to students before they sign up for majors.On the other hand,this paper designs and implements a professional triage algorithm integrating professional fitness.In the process of professional diversion,this algorithm is used to conduct professional diversion for students.On the premise of satisfying the preference of volunteers and selecting the best students,students are first divided into the majors with higher suitability in the adjustment stage,which to some extent solves a series of problems caused by students' inadaptability to the adjustment of maj ors in the follow-up study.In this paper,the requirements of the system are analyzed by using scenarios and roles,and the functional requirements and non-functional requirements of the system are proposed.Then,the system is designed,the architecture of the system is designed,the system is divided into six modules,and the detailed design of each module,the flow chart and class diagram of each module is given,according to the detailed design,the coding of each module is implemented.At last,the system test was carried out,and the test results were analyzed.Through the functional test and performance test of the system,the functional modules of the professional diversion system based on deep learning designed in this paper meet the expected standards,the system performance indicators meet the expected standards,and the system can be applied in the actual scene.