Abstract: In intelligent transportation systems, traffic flow prediction plays a crucial role. However, traffic data often exhibit significant dynamic spatial correlations and sudden pattern changes, making it difficult for traditional models to comprehensively capture spatiotemporal dependencies in real-world transportation networks. To address this challenge, a bidirectional diffusion dynamic graph convolutional traffic flow prediction model, BDDGNet, was proposed by integrating multi-source information and graph-level attention. The model integrates spatiotemporal embedding and various dynamic features, and utilizes a multi-head attention mechanism to construct dynamic adjacency relationships between nodes, capturing temporal evolution of spatial connectivity in traffic network. For spatial modeling, a bi-directional diffusion graph convolution module was designed to learn propagation dynamics of node states from both forward and reverse directions, enabling the collaborative modeling of spatial dependencies in traffic flow. In addition, a graph-level contextual attention mechanism was introduced to extract global semantic representations of key nodes, thereby enhancing model’s understanding of overall topological structure of traffic network. Experimental results on two real-world traffic flow datasets, PEMSD4 and PEMSD8, demonstrate that proposed BDDGNet achieves superior numerical performance for 15-minute, 30-minute, and 60-minute prediction tasks. The overall average results show that on PEMSD4, mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) reach 18.26, 30.26, and 12.16%, respectively, representing improvements of 25.2%, 19.3%, and 26.9% compared with Diffusion Convolutional Recurrent Neural Network (DCRNN) based on static graph structures. On PEMSD8, MAE, RMSE, and MAPE reach 14.07, 23.44, and 9.24%, corresponding to improvements of 16.3%, 11.1%, and 15.4% over DCRNN. These results indicate that proposed model can more accurately capture spatiotemporal dependencies in dynamic traffic environments and significantly enhance prediction accuracy.