As the urbanization process accelerates, the problem of traffic congestion is becoming increasingly severe. Efficient traffic flow prediction is crucial for traffic management and urban planning. This work proposes a spatiotemporal analysis model based on multi-order sampling neighbor aggregation, aiming to overcome the over-smoothing problem inherent in the traditional GCN multi-layer stacking. In the model construction, this work designs a spatial analysis module of multi-order sampling neighbor aggregation, which effectively solves the over smoothing problem and reduces the consumption of computing resources. In addition, this work also designs a time analysis module based on residual gated units, which uses a residual structure to complete the temporal analysis task of traffic data. The model is evaluated on real open-source datasets, and the results show that this model is superior to other comparative models, with a comprehensive error reduction of 6.34%.