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Road network structure integrated traffic flow situation prediction is a highly nonlinear and complexly spatial-temporal dynamic correlation time-series data prediction problem. However, traditional traffic flow situation forecasting methods cannot model the temporal and spatial correlation in long-term series data in the traffic network. To address the issue mentioned above, a deep learning model of traffic flow prediction based on graph structure is proposed. Firstly, the graph wavelet convolution operator is defined based on the graph wavelet transform. Furthermore, the graph wavelet convolution neural network module is designed based on the operator for the traffic flow situation prediction. Secondly, a spatial-temporal dynamic correlation model is constructed based on the spatial-temporal attention mechanism to capture the dynamic temporal and spatial correlation of the traffic network. Finally, the strategy of stacking multi-layer graph wavelet neural network modules is adopted to establish a novel graph wavelet neural network for road network traffic flow situation prediction. Experimental results show that the developed model??s performance on the experimental datasets is better than the existing baseline models. In the comparative experiment on the non-zero element statistics of the graph wavelet transform matrix and the Fourier transform matrix, it??s found that the convolution operation based on the graph wavelet transform is more sparse. Therefore, the convolution operation defined based on the graph wavelet transform is more helpful to improve the calculation efficiency of the traffic flow situation prediction model.