基于消费大数据的高校家庭经济困难学生认定分析与研究

Research on the Identification of Poor College Students Based on Consumption Big Data

探讨使用量化方法为高校精准扶贫工作提供理论依据和技术支持。后扶贫时代,相对贫困依旧存在,而家庭经济困难学生认定工作是高校扶贫工作的重中之重,也是各大高校在扶贫工作中容易遇到的难点。本文在校园一卡通消费的大数据基础上,利用数据挖掘技术构建学生消费特征数据,最终生成家庭经济困难生认定的聚类模型。通过与实际经济困难生数据做对比,发现本模型具有较高的识别率,能够为高校家庭经济困难生的认定提供技术支持,具有一定的实践应用价值。

Objective To explore the use of quantitative methods to provide theoretical basis and technical support for precision poverty alleviation in universities.In the post-poverty era,relative poverty still exists,and students from families with financial difficul⁃ties recognize that work is the top priority of poverty alleviation work in colleges and universities,and it is also a difficulty that major colleges and universities often encounter in poverty alleviation work.Methods Based on the big data of campus all-in-one card con⁃sumption,data mining technology was used to construct student consumption characteristics data,and finally a clustering model for the identification of students with financial difficulties in families was generated.Conclusion By comparing with the actual data of economi⁃cally disadvantaged students,it is found that this model has a high recognition rate and can provide technical support for the identifica⁃tion of students with economic difficulties in colleges and universities,and has certain practical application value.