Design And Implementation Of New College Entrance Examination Volunteer Recommendation System Based On Grey Forecast

As a mechanism for selecting new students for higher education institutions,the college entrance examination not only has the function of ensuring the quality of higher education and social equality,but also has the function of maintaining social stability and promoting the mobility of social classes.Volunteer reporting is a key problem faced by candidates after the college entrance examination.If the college entrance examination information is not fully grasped,problems such as high scores and low scores or repetitions will occur.Hebei Province,as one of the major college entrance examination provinces,is also a pilot province for the reform of the national college entrance examination system.In recent years,with the merger of the second batch of undergraduate and the third batch of undergraduate admissions,the promotion and implementation of the new college entrance examination model,and other changes in college entrance examination policies,the traditional college entrance examination voluntary filing model is gradually no longer applicable,so it is necessary to explore a new voluntary filing model.In addition,after investigation and research,it is found that the current acceptance probability prediction of the voluntary reporting system is not accurate,and the factors considered in the voluntary recommendation are relatively single,and there is no comprehensive method to recommend suitable volunteers for the candidates.Therefore,according to the new college entrance examination policy of Hebei Province,this article uses the college entrance examination data of the past five years to design and implement a voluntary recommendation system based on gray prediction.The main research work of the thesis includes the following four aspects.(1)Through web crawler technology and manual sorting methods,we collected and sorted out candidate data and college data in Hebei Province in the past 5 years,and cleaned,processed and integrated the data to build a college entrance examination data set for subsequent algorithm experiments and system construction Provide a data basis.(2)A comparative study of the methods of predicting scores found that the direct prediction of scores is not accurate.Therefore,this thesis selects the ranking prediction,using the collected data of the professional rankings of various colleges and universities,based on the gray prediction model GM(1,1)to predict The professional rank,thereby predicting the professional score.This thesis selects two schools,each with three majors for simulation evaluation.After calculation,the average relative error of the ranking of the 6 majors is 3.97%,and the average relative error of the scores is 0.17%,and the prediction effect is relatively accurate.(3)This thesis analyzes the various factors considered in the voluntary reporting,and uses the analytic hierarchy process to model these factors.Taking admission probability,personal evaluation,university rankings,and personal preferences as consideration factors,and the voluntary list as decision-making goals,design and construct a hierarchical analysis model,calculate the weight of each factor,and the judgment matrix passes the consistency test.(4)Constructed a new college entrance examination voluntary application recommendation system suitable for candidates in Hebei Province.The new college entrance examination is interpreted in the system,and subject selection information is added,which can quickly screen students who can fill in the volunteers.At the same time,the system has functions such as college information query,professional information query,admission probability prediction,personal evaluation,and voluntary intelligent recommendation.