Concerning the problems that the existing traffic flow prediction models cannot accurately capture the spatio-temporal features of traffic data， and most models show good prediction performance in single-step prediction， and the prediction performance of models in multi-step prediction is not ideal， a Spatio-Temporal Traffic Flow Prediction Model based on Gated Convolution （GC-STTFPM） was proposed. Firstly， the Graph Convolution Network （GCN） combining with Gated Recurrent Unit （GRU） was used to capture the spatio-temporal features of traffic flow data. Then， a method of splicing and filtering the original data and spatio-temporal feature data by using gated convolution unit was proposed to verify the validity of spatio-temporal feature data. Finally， GRU was used as the decoder to make accurate and reliable prediction of future traffic flow. Experimental results on traffic dataset of Los Angeles Highway show that compared with Attention based Spatial-Temporal Graph Neural Network （ASTGNN） and Diffusion Convolutional Recurrent Neural Network （DCRNN） under single step prediction （5 min）， GC-STGCN model has the Mean Absolute Error （MAE） reduced by 5.9% and 9.9% respectively， and the Root Mean Square Error （RMSE） reduced by 1.7% and 5.8% respectively. At the same time， it is found that the prediction accuracy of this model is better than those of most existing benchmark models under three multi-step scales of 15， 30 and 60 min， demonstrating strong adaptability and robustness.