Abstract: During the evolution of public opinion in group social networks, it is difficult for the current methods to obtain the data in key nodes, resulting in the inability to accurately obtain parameters such as the number of public opinion propagation, search index, time to reach the peak of public opinion, and the problem of low evolution accuracy. A simulation method of public opinion evolution in group social network based on clustering algorithm and SIS(Susceptible Infected Susceptible Model) model is proposed. The PageRank algorithm is used to obtain the key nodes, and the clustering algorithm is used to cluster the data in the key nodes. The SIS model is constructed, and the public opinion evolution simulation of the group social network is completed through the SIS model. The experimental results show that the proposed method can accurately obtain the parameters such as the number of public opinion propagation, search index and the time to reach the peak of public opinion, and the evolutionary simulation accuracy is high.

Key words: clustering algorithm, susceptible infected susceptible ( SIS ) model, key node identification, PageRank algorithm, evolution of network public opinion