Precise prediction modeling of university social network public opinion evolution trend

Abstract

Due to the poor analysis of the evolution cycle and cycle theory structure, the prediction accuracy of public opinion in university social networks is low. Thus, a new prediction model for the evolution trend of public opinion in university social networks is proposed. By analyzing the theoretical structure of the public opinion evolution cycle and the life cycle of social networks in colleges and universities, the public opinion evolution cycle is determined. By using the E-Divisive algorithm, the evolution trend of public opinion is divided. The trust degree of different views on the network platform and the characteristics of public opinion events are abstracted, and the public opinion evolution trend prediction model is established to predict the evolution trend of social network public opinion in colleges and universities. The experimental results show that the relative error of this prediction model is lower than that of the traditional model. The error value is less than 0.5, which indicates that the prediction accuracy of this prediction model is higher, which is conducive to creating a healthy social network platform for colleges and universities and promoting the healthy development of college students' bodies and minds.