Aiming at the diverse and complex factors affecting expressway traffic flow, this study proposes a Bi-LSTM prediction model based on multiple factors. Firstly, the original data are cleaned up and analyzed with respect to their correlation to improve the research accuracy and reduce the data dimension. Secondly, a multi-factor time series matrix for traffic flow is constructed based on the time sliding window and the proposed model is trained and optimized with MAE and RMSE as the evaluation indicators. This model considers high-correlation influencing factors such as weather conditions, holidays, and toll, as well as changes in the preorder and postorder of traffic flow. With the expressway toll data in Shaanxi Province as the object, the results show that the proposed model is more applicable and accurate than GRU and LSTM in the short-term prediction of expressway traffic flow.