The key point of graph convolutional networks applying to traffic flow forecasting is to construct the Laplacian matrix. Nevertheless, most available methods mainly rely on the spatial distance among nodes to construct Laplacian matrix, which limits the wide application of the model. In this paper, we propose a dynamic spatial-temporal graph attention graph convolutional networks (GAGCN) method to improve the applicability of the model. The Laplacian matrix in this model is constructed directly by the dependencies among the nodes hidden in the traffic data which are identified by the graph attention network, and can be dynamic adjusted over time.