Forecasting the traffic flows is a hot issue for researchers and practitioners in the transportation field. It is very challenging to forecast the traffic flows due to the high nonlinearity and complexity of the data, and most of the existing methods cannot effectively capture the spatial-temporal correlations of traffic flow data. In this paper, we propose a novel deep learning based model, multi-component spatial-temporal graph convolution networks (MCSTGCN), to solve the problem of traffic flow forecasting. MCSTGCN employs three components to respectively model the recent, daily and weekly characteristics of traffic flow data. Each component uses graph convolutions in the spatial dimension and convolutions in the temporal dimension to effectively capture the spatial-temporal correlations of traffic data. Experiments on a public California freeway dataset show that the prediction performance of the MCSTGCN model is better than other existing prevalent methods.