高校贫困生精准资助的神经网络模型

Precision Subsidy Model for Impoverished University Students based on Neural Network

针对贫困生认定环节中存在的诸多问题,在大数据应用的背景下提出将基于神经网络的数据挖掘方法应用到高校贫困生识别管理之中.通过分析学生校园卡“一卡通”消费记录掌握学生的消费水平,更客观地评价学生的贫困程度,精准挖掘贫困生群体.首先对一卡通数据做预处理,然后提取特征,再进行神经网络模型训练,最后利用已知标签的数据验证模型的正确性.模型对于指导贫困生的精准助学工作,提高学生管理水平具有良好的研究意义和实用价值.

In view of the problems of identifying impoverished students, the data mining based on Neural Network is applied to the identification and management of impoverished students in the context of big data. Through the analysis of student campus card consump- tion records, the proposed method can master the students＇ consumption level, grain a more objective assessment of the students＇ poverty level, and find out the impoverished students accurately. Firstly, the pre-processing of the card data is done, then the features is extracted, and the model training is carried out. Finally, the data of the known tags is applied to testing and verifying the correctness of the model. The model is of great significance to guide the work of impoverished students and improve the management level of students.