Qualitative Prediction and Quantitative Analysis of Events in Educational Network Public Opinion Crisis: a Deep Learning Approach

Abstract

The crisis of educational network public opinion refers to issues such as public dissatisfaction and trust crisis caused by unexpected events in the field of education. The frequency and scope of the impact of educational network public opinion crisis are gradually expanding, bringing huge challenges to educational management departments and institutions. This article aimed to explore how to use deep learning technology to qualitatively predict and quantitatively analyze educational network public opinion crises. This article adopted experimental and comparative methods to qualitatively and quantitatively analyze the crisis of public opinion in educational networks, and obtained the characteristics of several deep learning methods. Experimental data showed that under the framework of convolutional neural network (CNN), the accuracy of public opinion crisis prediction in the sigmoid function was 99.9%; the loss rate was 0.34%, and the running time was 65 seconds.