Short-term traffic flow prediction is a key technology for intelligent transportation systems to achieve efficient management and real-time regulation. To address the shortcomings of existing methods in spatio-temporal dependency representation and error accumulation, a dual-path spatio-temporal graph attention network model was proposed to achieve more accurate and robust short-term traffic flow prediction. Firstly, a dual-path graph attention mechanism for both static and dynamic conditions was designed to extract spatial features. In the static attention branch, stable spatial relationships were built based on the physical topology of the road network, while in the dynamic attention branch, sudden spatial correlations were adaptively captured according to real-time traffic flow data. The two branches were weighted and fused through a learnable fusion parameter, and the influence between static topology and dynamic state was adaptively balanced, so that comprehensive spatial features were extracted. Secondly, a long short-term memory network encoder structure was adopted to hierarchically extract and flexibly map the features of time series. Finally, the decoder introduced a dynamic attenuation hybrid teaching-learning mechanism, integrating recursive and teaching decoding modes to predict traffic flow, in order to effectively prevent error accumulation. The PEMS04 and PEMS08 datasets were used for training, validation and testing. The results show that the model achieves approximately 4.23%, 3.18%, 6.90% and 3.89%, 2.59%, 6.44% improvements over the optimal spatio-temporal comparison model convolutional neural network-long short-term memory network(CNN-LSTM) in terms of mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) indicators respectively. It can be seen that the model is capable of effectively balancing the static topology and dynamic state of the road network, and significantly reduces error accumulation through the hybrid teaching-learning mechanism, demonstrating its superior performance and application potential in short-term traffic flow prediction.