In order to address challenges on how to accurately capture the spatial-temporal dependency， dynamic information and spatial heterogeneity information in traffic forecasting， we propose a time-series decomposition and attention graph neural network （TDAGNN） based traffic forecasting. Specifically， the model first adopts the dual time-series decomposition convolutional neural network （DTDCNN） to extract temporal dependency from traffic data. Secondly， the multi-head interactive attention （MIA） network is introduced to capture spatial heterogeneity and dynamicity from traffic data via the interactivity between the original features and the local augmentation features. Thirdly， the self-scaling dynamic diffusion graph neural network （SDDGNN） is introduced for capturing the spatial dependence and dynamicity from the traffic data. Finally， extensive experiments are carried out for some datasets. Experimental results demonstrate that the average MAE， RMSE and MAPE of the proposed model can be improved up to 14.64， 23.68 and 9.41% respectively， compared to other classic algorithms， proving its high prediction accuracy.