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According to the existing traffic congestion detection system of cities,a detection system is proposed to solve the problems of relatively low and unstable accuracy in processing the traffic monitoring data. This model integrated multiple random forests(RF) to process each node data in the traffic network parallel,then a cascade classifier is designed to recognize the traffic network status. At last,the importance of node in the traffic network is assessed by using RF. The implementation of this model mainly consisted of three levels,that is,feature extraction,building the integrated classification model and combination analysis. Comprehensive performance of the model is analyzed under different size traffic network. and compared respectively with other algorithms. Finally,experiments show the proposed model not only has better comprehensive performance in traffic network monitoring data,but also can be adapt to the change of network size. This model provides an application model for traffic congestion detection.