Recognition and Analysis of Poor Students on College Students Campus Card Consumption Data Based on Big Data

Subsidization for students from low-income families is a major student management work for colleges and universities. With deep and extensive use of big data in all sectors, relevant big data shall be used to identify poor students. Using big data rationally for targeted subsidization represents in-depth application of big data in educational field. In this thesis, the researchers collected 36546 data concerning dining consumption of students in three months, used Datist, a big data analysis software to build a model, acquired concerning dining habits, consuming behaviors, situations in school and consumption indicators of the students, and then selected poor students. This study laid a solid foundation for large-scale dynamic implementation of "campus big data and targeted subsidization", accurate recognition of poor students and rational student analysis in future.