With the rapid development of the Internet, how to effectively monitor and guide the public opinion on the Internet is of great significance to the social stability. The prediction of network events’ hotness is an important part of public opinion supervision. In view of the fact that the traditional method ignores the temporal information and the relevance contained in the event time series in the process of prediction, a prediction model based on EKSC algorithm is proposed. The model uses the EKSC algorithm to cluster the time series of known network public opinion events of each class and construct a class model library. The time sequence of the know hotness in the predicted event is scaled. The least square method is used to predict the event by selecting the model with the minimum mean square error in the class library. Experimental results show that this method can effectively predict the hotness of network events.