An Applied Research of Big Data in the Work of Higher Education Students: Taking the Analysis of Finding Loneliness and Their Characteristics as an Example

This article is an applied research that combines the technology and methods of big data with student work. This study used the canteen consumption data of freshman to junior students in a university in Guangdong from September 2018 to December 2020. And found the suspected lonely in the student group by constructing a co-occurrence network. The results showed that: (1) the frequency of co-occurrence among freshmen to junior students shows a decreasing trend, and the frequency of co-occurrence of freshmen friends was significantly higher than that of sophomores and juniors; (2) The number of college students who are suspected of being "lonely" increases with grades; (3) The average consumption level of students who are suspected of being "lonely" is significantly lower than that of students who are "non-lonely"; (4) Students who are suspected of being "lonely" are more inclined to use meal cards for consumption rather than WeChat/Alipay; (5) The degree of intimacy among freshman students is significantly higher than that of sophomores and juniors.