全文下载：
202002016.pdf
文章编号： 1672-6987（2020）02-0113-06； DOI： 10.16351/j.1672-6987.2020.02.016
于超， 刘国柱*
（青岛科技大学 信息科学技术学院,山东 青岛 266061）
摘要： 高考志愿填报是影响学生未来发展的一个重要转折阶段，目前中国的高考志愿填报和录取机制还存在些许不足，“高分低录”“高分落榜”及未能选择到自己喜欢的专业等问题仍然存在。为了使考生能够选择自己理想的学校和专业，本工作提出了基于大数据分析的高考志愿填报算法。首先，通过对用户110道问答题目答案采集，找到用户对问题答案的最近邻集合，然后，采用支持向量机算法建立高考志愿填报模型，最后通过具体仿真对比试验对算法有效性和优越性进行分析。实验结果表明：此算法提高了高考志愿填报精准度；分析结果明显优于当前其它高考志愿填报算法。
关键词： 高考志愿填报； 最近邻集合； 支持向量机； 灰色关联分析法； 推进算法
中图分类号： TP 399文献标志码： A
引用格式： 于超， 刘国柱. 基于大数据分析的高考志愿填报算法\[J\]. 青岛科技大学学报（自然科学版）， 2020， 41（2）： 113-118.
YU Chao，LIU Guozhu. Research on algorithms in the form filling-in for college entrance based on big data analysis\[J\]. Journal of Qingdao University of Science and Technology（Natural Science Edition), 2020， 41（2）： 113-118.
Algorithms in the Form Filling-in for College Entrance Based
on Big Data Analysis
YU Chao， LIU Guozhu
(College of Information Science and Technology, Qingdao University of Science and Technology, Qingdao 266061,China)
Abstract: For high school graduates, it′s an important turning point in their development to fill in college entrance voluntary table. There exists, however,drawbacks in China′s mechanism of voluntary filling-in and enrollment in college entrance examination. Within such context, students with high scores may be admitted to lower-ranking universities, or even fell out of the list; besides, some missed the preferred major. In order to enable candidates to choose their ideal universities and majors, the paper, based on big data analysis, describes some algorithms applied in the application. It can be divided into three steps: firstly finding the nearest neighbor set of their answers after collecting users′ response to 110 questions, secondly building a model of filling-in algorithm dependent on support vector machine（SVM）, and finally analyzing the effectiveness and superiority of the algorithm by means of simulation and comparison experiment. According to the results that are obviously superior to other kinds, this algorithm greatly improves the accuracy in the form filling-in.
Key words: college entrance examination voluntary filling; nearest neighbor set; support vector machine; grey relational analysis method; progressive algorithms
收稿日期： 2019-01-15
基金项目： 山东省教研项目（Z2016Z004, M2018X130, 201801243014）.
作者简介： 于超（1994—），男，硕士研究生.*通信联系人.
