In order to alleviate the increasingly serious traffic congestion problem, realize intelligent traffic control, provide accurate real-time traffic flow prediction data for traffic flow induction and traffic travel, an LSTM-BP combined model algorithm based on long-short-time memory neural network (LSTM) and BP neural network is designed. Mining the characteristic factors of known traffic flow data, establishing the framework of time series prediction model, and using Matlab to complete the simulation from the data processing to the model simulation to realize the accurate prediction of short-term traffic flow based on LSTM-BP. Compared with the three prediction network models of LSTM\BP\WNN, the results show that the time series predicted by LSTM-BP has higher accuracy and stability. The construction of the model can provide basis and reference for the prediction of traffic distribution, the division of traffic modes, and the distribution of real-time traffic flow.