Accurately predicting traffic flow is conducive to optimizing traffic management and improving traffic efficiency, a new dynamic-static graph fusion and temporal flow attention network is proposed. The graph convolutional network is used to capture dynamic and static spatial correlations. A flow attention mechanism is introduced to effectively alleviate the quadratic complexity problem. A temporal correlation modeling (TCM) module is designed to replace the linear transformation method of the flow attention mechanism, so as to enhance the model's temporal modeling ability. A large number of experiments are carried out on four real-world traffic datasets. The results show that the proposed model has superior performance and significantly outperforms the baselines.