The basis of Intelligent Transportation Systems (ITS) is traffic flow forecasting. This is important for reducing congestion on the road. The complexity of traffic movements and their spatial and time-dependent dependencies make it difficult to accurately predict traffic flows. This paper proposes a new Spatiotemporal Interactive Dynamic Synchronous Graph Convolution Network for Traffic Flow Forecasting. It is made up of Interactive Dynamic Graph Convolution Networks (IDGCN) as well as the Spatiotemporal Synchronous Graph Convolution Layers (SSGCL). IDGCN learns and shares features using an interactive learning strategy that is based on Dynamic Graph Convolution (DGCN). SSGCL effectively captures the intricate local and dynamic correlations of traffic flows by designing numerous Spatiotemporal Synchronization Graph Convolution Modules (STSGCM) for different time periods. The experiment results show that the STIDSG models suggested in this article extract the spatiotemporal characteristics of traffic flows and perform better than the baseline methods commonly used for prediction.