Intelligent Recommendation Model For Voluntary Reporting Of College Entrance Examination For Poverty Alleviation In Education And Applied Research

In recent years,with the reform of the college entrance examination,the process of will-filling for college has become technically important.Thus,during the period of college entrance examination will-filling,some training institutions even draw the attention of candidates and parents by advocating "big data" and "artificial intelligence" to earn high service fees.However,for candidates and parents in impoverished areas with information blockages and underdeveloped economies,they can only rely on their own subjective assertions and limited information to fill in the wills.Therefore,there are numerous phenomena that some candidates get a downshift admission,don’t be admitted to their ideal universities,or have to attend the next year’s examination because they are not accepted by even a single university.In response to the national policy of “precise poverty alleviation” to alleviate poverty through education,this paper took the rural students in the central Hunan region as an example to explore the intelligent recommendation model for will-filling in the college entrance examination.The specific work includes:(1)This paper focuses on the screening model of college admissions results based on neural network.The sample data set was first determined by sample surveys and extensive experimentation.The sample data contains four attributes: the scores of the candidates in 2018,the ranking of the province,the enrollment scores of the universities in Hunan Province in 2017,and the rankings of admissions.The BP algorithm was used to model the sample data,and the identification rules were obtained to train the best neural network.(2)Through a questionnaire survey of some students who have established a file in Loudi No.1 middle school,11 indicators of colleges and universities in the middle of Hunan were found to appeal to candidates most.AHP was applied to model the 11 indicators according to the subjective setting of candidates,and the dynamic weight of each indicator was obtained.With the combination of the weight of indicators and the information of colleges and universities,the recommended value of colleges and universities was calculated.(3)The intelligent service system model of the college entrance examination will-filling was designed.Candidates can filtrate the possible universities to be admitted by inputting scores and provincial rankings in the neural network.Through the analytic hierarchy process,the dynamic index weights were obtained to comprehensively give 10 recommended universities that best meet the actual situation of the candidates.The experiment proves that the final recommendation results meet the three requirements of the candidates’ regular will-filling.In summary,the intelligent recommendation algorithm proposed in this paper is feasible and effective.