<p>Accurate traffic flow forecasting serves as a critical technical foundation for building intelligent transportation systems and enhancing road network operational efficiency. Traffic flow patterns exhibit significant multi-time-scale characteristics, encompassing both long-term macroscopic trends and short-term fluctuations. The dynamic interactions across these scales constitute a core challenge in traffic prediction. Although a few studies have considered multi-scale feature extraction, they often adopt fixed paradigms to process information from different temporal scales, failing to effectively model the dynamic correlation mechanisms among local temporal patterns at multiple scales as traffic conditions evolve. Furthermore, dynamic spatial dependency modeling based on graph convolution is often hampered by the over-smoothing of neighborhood information, which weakens the discriminative power of critical spatio-temporal features and limits the model’s adaptability to complex traffic scenarios. To address these issues, we propose a Spatio-Temporal Dual-Graph Fusion Network (STDGFN). The model first partitions traffic flow sequences based on multi-scale time windows, encoding each segment into a temporal pattern representation that captures local contextual information. It then employs stacked Spatio-Temporal Feature Extraction Blocks (STFEB) to capture complex spatio-temporal dependencies at varying granularities, including dynamic interactions among multi-scale temporal patterns and spatial correlations between nodes. To mitigate the over-smoothing caused by deep graph convolution, we design a Spatial-Aware Gated Pattern Enhancer (SAGPE) that integrates node-level spatial attributes into a gating mechanism, effectively reinforcing the spatial heterogeneity of each node. Experimental results demonstrate that STDGFN outperforms existing state-of-the-art baseline methods on five real-world traffic datasets.</p>