Prediction method in student relationship network based on Big Data Mining
Xinhua Chai · 2021 IEEE 2nd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA) · 2021
In some universities, there will often be a Matthew effects problem of student groups, can scientifically explain this phenomenon, and make the best management prediction program, this is a hot problem of the school. By building a student group interaction's social network, the paper combines complex network characteristics with big data mining technology to establish prediction method to achieve predictions in the development trend of key nodes in the network. The prediction method mainly consists of two steps. The first is to use time series analysis to find groups with poor performance; it is used to explain the Matthew effects phenomenon in the social relationships network with uncertain. The second according to the first step's research, build predictive algorithms in complex networks, and give some suggestions in moral education and style education. The paper in theory puts forward a new prediction algorithm on the complex network, which provides targeted strategies for the management of college student education in application.