Error comparison.

Since the dissemination of information is more rapid and the scale of users on online platforms is enormous, the public opinion risk is more visible and harder to tackle for universities and authorities. Improving the accuracy of predictions regarding online public opinion crises, especially those related to campuses, is crucial for maintaining social stability. This research proposes a public opinion crisis prediction model that applies the Grey Wolf Optimizer (GWO) algorithm combined with long short-term memory (LSTM) and implements it to analyze a trending topic on Sina Weibo to validate its prediction accuracy. A full-chain analytical framework for online public opinion prediction is established in this study, which enables the model to illustrate the level of risk related to public opinion and its variation trend by introducing the public opinion risk index. The prediction accuracy of the model is validated through several evaluation criteria, and a comparison between real and predicted results, and the simulation of the intervention on this incident indicates that the proposed model is competent for both trend prediction and assisting in intervention. The study also demonstrates the importance of immediate response and intervention to public opinion crises.