Traffic prediction is one of the research focuses in intelligent transportation. This study proposes a short-term traffic flow prediction model based on discrete wavelet transformation (DWT) and graph convolutional networks (GCNs) to deeply explore the temporal and spatial features of traffic flow sequences and improve prediction accuracy. Firstly, the original traffic sequences are decomposed into detailed and approximate components by the DWT algorithm to reduce the non-stationarity of traffic flow data. Secondly, the adjacency matrix of the GCN model is optimized by introducing the distance factor term to extract the spatial features of road networks. Finally, each group of components decomposed by DWT is used as the input of the GCN model separately for prediction, and the prediction results of each group are reconstructed to obtain the final prediction value. The model is tested on the Caltrans PeMS dataset, and the test results reveal that compared with the ARIMA, WNN, and GCN model, the proposed model has reduced its mean absolute error (MAE) and mean absolute percentage error (MAPE) by 57% and 59%, respectively, which proves to be an effective method for predicting short-term traffic flow.