To address the limitations of the existing traffic flow prediction models in utilizing fusion information of traffic regions and node layer features effectively， as well as these models’lack of dynamic representation of spatio-temporal features， a Double-layer Multi-scale Dynamic Graph Convolutional Network （DM-DGCN） was proposed for urban traffic prediction. Firstly， a double-layer network architecture was adopted to fuse the spatio-temporal features of regions and nodes， so as to handle node and region traffic flow data simultaneously. Secondly， in the spatial dimension， a Spatial-dynamic Graph Convolutional Network （S-GCN） module was constructed to capture dynamic spatial correlations. Thirdly， in the temporal dimension， a Multi-Scale Temporal Convolutional Network （MSTCN） module was designed to capture potential temporal dependencies under different semantic environments. At the same time， the idea of spatial relationship learning was introduced into the temporal domain by designing a Temporal-dynamic Graph Convolutional Network （T-GCN） module， so as to construct a dynamic time-varying temporal relationship matrix. Finally， a dynamic fusion module based on the attention mechanism was designed to integrate region and node layer features， and the final prediction results were generated through the fusion layer. Experimental results on the Jinan and Xi’an traffic datasets show that for the 60-minute prediction task， DM-DGCN model reduces the Mean Absolute Error （MAE） and Root Mean Square Error （RMSE） by 5.79% and 5.56%， respectively， compared to Spatio-Temporal Pivotal Graph Neural Network （STPGNN） model on the Jinan dataset； and by 3.73% and 4.19%， respectively， compared to Hierarchical Spatio-Temporal Graph Ordinary Differential Equation network （HSTGODE） model on the Xi’an dataset. The above verifies that DM-DGCN model outperforms the existing baseline models， captures dynamic multi-scale spatio-temporal dependencies in traffic data effectively， and predicts future traffic flow accurately.