This paper proposes a traffic flow prediction model based on multi-view spatiotemporal convolution, aiming to address the challenges of traffic flow forecasting in intelligent transportation systems. Traffic flow prediction is crucial for urban traffic scheduling, logistics efficiency, and public travel planning. The model combines graph convolutional networks (GCNs) and gated recurrent units (GRUs) to capture both spatial and temporal dependencies in traffic data. The GCN captures spatial features by modeling the road network topology, while the GRU captures temporal features by modeling the dynamic changes in traffic data. The model is evaluated on two real-world datasets: the SZ-taxi dataset and the Los-loop dataset. The results show that the T-GCN model outperforms other baseline methods in terms of prediction accuracy and spatio-temporal feature extraction. The model achieves high prediction accuracy across various prediction horizons, demonstrating its effectiveness in spatio-temporal traffic prediction tasks. The T-GCN model is able to capture both temporal and spatial characteristics of traffic data, leading to more accurate predictions compared to models based on single factors. The model's ability to detect rush hour patterns and predict traffic congestion highlights its practical value in traffic management. The study also emphasizes the importance of machine learning and deep learning in traffic flow prediction, particularly in handling complex spatio-temporal dependencies. The T-GCN model provides a promising solution for improving traffic management and urban planning.This paper proposes a traffic flow prediction model based on multi-view spatiotemporal convolution, aiming to address the challenges of traffic flow forecasting in intelligent transportation systems. Traffic flow prediction is crucial for urban traffic scheduling, logistics efficiency, and public travel planning. The model combines graph convolutional networks (GCNs) and gated recurrent units (GRUs) to capture both spatial and temporal dependencies in traffic data. The GCN captures spatial features by modeling the road network topology, while the GRU captures temporal features by modeling the dynamic changes in traffic data. The model is evaluated on two real-world datasets: the SZ-taxi dataset and the Los-loop dataset. The results show that the T-GCN model outperforms other baseline methods in terms of prediction accuracy and spatio-temporal feature extraction. The model achieves high prediction accuracy across various prediction horizons, demonstrating its effectiveness in spatio-temporal traffic prediction tasks. The T-GCN model is able to capture both temporal and spatial characteristics of traffic data, leading to more accurate predictions compared to models based on single factors. The model's ability to detect rush hour patterns and predict traffic congestion highlights its practical value in traffic management. The study also emphasizes the importance of machine learning and deep learning in traffic flow prediction, particularly in handling complex spatio-temporal dependencies. The T-GCN model provides a promising solution for improving traffic management and urban planning.