Aiming at the problem of insufficient characterization of road network spatial structure and inadequate mining of traffic flow spatiotemporal characteristics in the traffic flow prediction model, a new type of directed spatiotemporal graph is constructed. In which the spatial relationship between nodes are characterized by defining the relative proximity, and the spatiotemporal relationship between nodes are characterized by learning the influence weights of neighborhood nodes on the prediction node, so as to better express the temporal and spatial characteristics of traffic flow. Taking spatiotemporal graphs as the input of the prediction model, graph convolution is used to obtain the spatial dependence of traffic flow data, and the gated recurrent neural network is used to obtain the spatiotemporal dependence of traffic flow data, and a traffic flow based on the spatiotemporal graph convolution recurrent neural network(STG-CRNN) is established. The model prediction effect is verified on the U.S. highway traffic data set, and the results show that the STG-CRNN model is better than the autoregressive moving average model, the gated recurrent unit model, and the diffusion convolutional recurrent neural network model in terms of the mean absolute error, root mean square error, and mean absolute percentage error.