Graph-constructed traffic flow prediction has recently achieved significant advances in transportation research. Existing methods predominantly rely on predefined spatial adjacency graphs to model spatio-temporal relationships. However, these static adjacency matrices inadequately represent the complex spatio-temporal correlations among road network nodes and fail to capture dynamic interactions that evolve over time. This paper proposes a novel traffic prediction model called the Spatial-Temporal Dynamic Multiscale Graph Convolutional Network (SDMGCN). The SDMGCN first models the dynamic properties of node spatial correlations through attention mechanisms, constructing the Dynamic Interaction Perception Graph (DIPG). It then innovatively proposes the Multi-Order Augmented Graph Convolution Module (MOAGCM), which adaptively adjusts node weights through a multi-order information aggregation mechanism. When combined with the DIPG, it captures deeper dynamic spatial dependencies between nodes. Finally, the multiscale time-gated convolution module captures temporal dependencies at various time scales. Experimental evaluations on two real-world traffic datasets demonstrate that the SDMGCN model significantly outperforms state-of-the-art methods.