Abstract: Most of the current research about public opinion on the network focuses on the analysis of emergencies' spreading and early-warning process, ignores the user's main role on the procedure of opinion-spreading. In terms of this problem, we introduced the concept of“concept space", modeled and simulated the users’concept clustering process during the transmission of emergencies on the network by using particle swarm optimization algorithm. On the basis of users' clustering results, we analyzed the dynamic evolution model of network emergencies. Changing the pa- rameter of velocity controls the convergence rate of user-clustering, and coordinates the evolution process of network c- mergencies, realizes the recognition of network hot events and early-warning for public opinion crisis. At last, we simu- lated the users' clustering behavior relatively based on basic PSO and speciation PSO (SPSO) algorithm Simulation re- sups show that SPSO algorithm is able to simulate the concept clustering more effectively. It is able to find multi-elute ring center at the same time, and is good to set adaptively coping strategies for early warning of public opinion.

Key words: Concept space, Particle swarm optimization, Speciation algorithm, Public opinion crisis