Short-term traffic flow prediction is not only related to historical data， but also affected by the traffic of adjacent areas. Since the trend and spatial correlation of traffic flow are ignored by traditional Time Series Decomposition （TSD） models， a time series processing model based on the combination of Time Series Decomposition and Spatio-Temporal features （TSD-ST） was proposed. Firstly， the trend component and periodic component were obtained by using Empirical Mode Decomposition （EMD） and Discrete Fourier Transform （DFT）， the Spatio-Temporal （ST） correlation of the fluctuation component was mined by Mutual Information algorithm （MI）， and the state vector was reconstructed on the basis of the above. Then， the fluctuation component was predicted by using the state vector through Long Short-Term Memory （LSTM） network. Finally， the final predicted value was obtained by reconstructing the prediction results of the three parts of the sequence. The validity of the model was verified on the real data of Interstate I090 in Washington State， USA. Experimental results show that the Root Mean Square Error （RMSE） of the proposed model TSD-ST-LSTM is reduced by 16.5%， 34.0%， and 36.6% compared with that of Support Vector Regression （SVR）， Gradient Boosting Regression Tree （GBRT） and LSTM， respectively. It can be seen that the proposed model is very effective in improving prediction accuracy.