Challenge of designing and building of the current Network Intrusion Detection System not only improves the ability for discriminating the improper internet behaviors, but also considers the plenty of computer resources which will be cost during the analysis of the network packets and behaviors. During the establishment of a fast intrusion detection system, the major purpose of the research is to intensify the data handling capacity when the network management system faces the mass network behavior. In the research, the modules stored in the network packets of the intrusion detection system are analyzed, and then the network flow data by applying the clustering algorithm based on the ant colony system is classified. Finally, a kind of algorithm which can remove repetitive computation is designed so that the flow can accelerated and the velocity can be distinguished. Experimental results show that the proposed fast clustering algorithm can significantly reduce the original computation time while sacrificing or promoting a very small accuracy, and then the computation speed of the intrusion detection system can be accelerated.