基于DNN网络结构的学院转专业生源研究

Research on Source of College Students Changing Majors Based on DNN Network Structure

针对目前高校转专业分配效率低,需要提前预测报考情况的问题,提出一种基于DNN(Deep Neural Network)网络结构下的预测模型。并以吉林大学2003年-2017年热门学院学生转专业情况建立预测模型;引入DNN深度学习网络结构,在谷歌研发的Tensorflow框架下建立高校热门学院转专业生源数量预测模型;最后,采用训练已有15年的数据对2020年的热门学院转专业生源数量进行预测分析。数据分析结果表明,所提方法较好地解决了热门学院转专业报考人数预测的问题,对后续工作开展具有一定的指导意义。

In colleges and universities,the registration of major transfer is very popular,and it is often difficult to allocate.Therefore,making preparations in advance is important in major transfer.If we can predict the enrollment of major transfer students in that year,it will be of great help to the follow-up work of colleges and universities.Popular college students to professional enrollment of Jilin University from 2003 to 2017 is used to establish the number of popular college students forecast model;the DNN(Deep Neural Network)deep learning network structure is introduced in the Google research and development of tensorflow framework to establish the number of popular college students forecast model;finally,the training data for 15 years is used to predict the number of popular college students in 2020 analysis.The method proposed can better solve the problem of the number of candidates for major transfer in popular colleges,and has a certain guiding significance for the follow-up work.