Abstract: In recent years,as the number of high school graduates growing,the demand of college entrance filling is increasing.But faced with massive amounts of college entrance data,students always cannot make reasonable decisions conform to their own will in a short time,resulting in filling accident.To address this issue,on the basis of crawling college entrance history data by web spider,a novel college entrance filling recommendation algorithm based on score line prediction and multi-feature fusion(Reco-PMF) is proposed.Firstly,BP neural network is applied to predict all the colleges admission lines of current year.Then,combining with colleges’ rankings,an admission probability algorithm is constructed on the basis of three score related features.Genetic algorithm is employed to optimize the weights of above features.On this basis,recommendation-score is defined to measure admission risk.Finally,a college filling list with multi-admission risk is generated.Experiment results show that,the college admission line prediction algorithm based on BP neural network performs better than other algorithms under all error bounds.Compared with existing on-line services of Baidu and Kuake,Reco-PMF increases the acceptance rates by 14.8% and 24.1%,and improves the average ranking of recommended colleges by 99 and 87 in accepted colleges.

Key words: College entrance filling, Score line prediction, Parameter optimization, Multi-weight, Genetic algorithm