A prediction method based on a time series prediction model combined with a data encoding and decoding mechanism was proposed for the situation that the topological information of the road network is incomplete, and it is not possible to realize the spatio-temporal traffic flow prediction. The chain structure of road network information was obtained by encoding the traffic flow data of road sections in the network, so as to obtain the topological information in the road network structure. The traffic flow prediction of the chain structure was carried out by the time series model. The extraction of the temporal features of the chain structure was completed. Finally, the spatio-temporal traffic flow prediction results of the road network were obtained by the decoding method. Different road networks and their GPS data were selected for comparison experiments. The spatio-temporal traffic flow prediction method of data encoding and decoding was introduced for comparison with the time series model and the baseline models of HA and ARIMA. The experimental results show that the performance of the deep learning model is significantly improved after the data encoding and decoding mechanism is introduced. The performance of the deep learning model with this mechanism is superior to that of the baseline model. The proposed method can achieve spatio-temporal traffic flow prediction only using a simple time series deep network combined with the data encoding and decoding mechanism.