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Accurate traffic flow prediction can provide decision-making basis for traffic management departments and early warning of road conditions for drivers, which is a crucial issue in the field of transportation. In recent years, related studies have used the characteristics of graph convolution neural network (GCN) in processing non-Euclidean structure data to model the spatial correlation of traffic flow data from complex road networks. However, existing traffic flow forecasting methods based on graph convolution fail to fully consider the directionality and dynamics of spatial correlation. Considering that dynamic traffic flow presents stable spatial correlation constrained by fixed road structure and dynamic spatial correlation influenced by traffic environment changes, this paper proposes an end-to-end two-stream graph convolution network (TSGCN) for dynamic traffic flow forecasting. Firstly, real-time traffic flow data are decomposed into stable components and dynamic components with different spatial correlations. Specifically, the stable components are constrained by the physical road network and traffic habits, while the dynamic components represent the fluctuations caused by changes in traffic conditions (such as traffic congestion and bad weather). Then, the stable and dynamic spatial correlations are extracted through the two-stream graph convolution layer. Finally, this paper uses the parameterized skip connection to fuse the spatial-temporal correlations to obtain the final prediction results. Experimental results on two published real-world traffic flow da-tasets show that the proposed model is better than several popular baselines.