Identification of Key Nodes in Public Opinion Communication by Integrating Knowledge Graph

In this paper, in view of the shortcomings of traditional methods of identifying key nodes in public opinion dissemination, such as low identification accuracy, low coverage of key nodes, and shallow central relevance, an optimization method based on knowledge graph is proposed. By collecting public opinion data on Weibo and building a social network, combining the community discovery algorithm with the improved PageRank algorithm, the effective identification of key nodes is realized. Experimental comparison shows that in the case of opinion leaders accounting for the top 3%, the coverage rate of key nodes of this method can reach 46.5%, and the recognition accuracy rate reaches 94.1%, which is significantly better than the hierarchical analysis method (coverage rate 21.2%, accuracy rate 90.7%) and the clustering analysis method (coverage rate 15.8%, accuracy rate 91.1%).Therefore, this study shows that knowledge graph technology can effectively improve the identification performance of key nodes of public opinion dissemination. The research in this paper confirms that the method of integrating knowledge graph can significantly improve the identification accuracy and coverage rate of key nodes of public opinion dissemination, and provides a powerful tool for public opinion management.