The invention discloses an urban traffic situation identification method based on a directed graph convolutional neural network. The method comprises the steps: carrying out the traffic situation classification of historical traffic flow information, converting an urban road network into a directed graph according to a point-edge conversion rule, and extracting a corresponding sub-graph; then, calculating the weight of a directed edge and the weight between non-directly connected nodes, standardizing the number of nodes of the subgraph, and calculating a traffic information matrix and a feature matrix of the subgraph; finally, designing a traffic directed graph convolutional neural network model, performing training and testing, using the model for classifying real-time traffic flow information to identify the real-time traffic situation of all road sections. According to the method, the incidence relation between directed road sections of different levels and different grades under the hybrid road network is fully considered, a unified standardized model input and traffic situation recognition model is designed, and good universality is achieved; moreover, the method has the characteristics of simple process, easiness in calculation, easiness in programming realization and the like, and can be suitable for complex urban road networks.