The impact of network behavior on social security and stability is increasing day by day. A computer system is designed to automatically collect, analyze, process, and warn of network behavior, automatically respond to real-time collected network behavior, timely resolve public opinion crises, and achieve the transformation of network behavior from passive prevention to active guidance. As an important technology for analyzing network behavior, the limitations of the Girvan-Newman algorithm are becoming increasingly apparent. This article first constructs evaluation indicators for community mining and then studies the traditional Girvan-Newman algorithm. Based on this, an improved Girvan-Newman algorithm is studied to address the problems of the traditional Girvan-Newman algorithm, and systematic testing and experimental analysis are conducted. The results indicate that the improved Girvan-Newman algorithm has superiority for large-scale network public opinion analysis.