Analysis of Behavioral Characteristics Based on Student's Personal Big Data

With the continuous improvement of the information construction of colleges and universities, the daily life and learning behaviors of college students are recorded and stored by major business systems, and they are accumulated, which has initially formed a large-scale and multi-type student personal big data environment. This paper mainly classifies and summarizes the students' data from the three aspects of student basic information, campus learning and campus life. It focuses on the feature extraction and index mining of students' campus consumption, curriculum and performance data, and constructs the student's personal big data behavior analysis model. In-depth analysis and mining of student consumption behavior data to explore students' dietary rules and consumption level. Through data analysis, the following rules were found: 1)The total number of students eating at school decreases year by year, and the breakfast rate decreases year by year; 2) Freshmen are one hour ahead of the "peak period" of breakfast meals for the whole group;3) The students' academic scores are highly correlated with the meal rate, breakfast meal rate and eating consumption level, and are less correlated with variables such as window selection stability, etc. 4) The more regular the student's diet, the more stable the level of consumption, and the higher the level of learning effort, the better the student's academic performance.