In order to improve the prediction accuracy and speed of short-term traffic flow, based on the instability and randomness of the traffic flow sequence, fast ensemble empirical mode decomposition ( FEEMD) and natural selection adaptive mutation particle swarm optimization algorithm (SAPSO) are proposed to optimize the two-way Predictive model combined with long and short-term memory network (BiLSTM). Firstly, using FEEMD to decompose the original unsteady traffic flow sequence into multiple stable intrinsic modal components (IMF) and residual components (Res), and filter out the noise part to improve modeling accuracy; secondly, introducing composite Multi-scale permutation entropy (CMPE) to detect the randomness of traffic flow sub-sequences and regroups them to simplify model construction and improve prediction accuracy; then, using BiLSTM to predict the reorganized subsequences, and use SAPSO to optimize the weights and thresholds of BiLSTM to further improve the prediction accuracy and prediction speed of the combined model; finally, the prediction values of each sub-sequence are superimposed to obtain the final prediction value. The experimental results show that the root mean square error of the FEEMD-SAPSO-BiLSTM combined model is 22. 9% and 54. 3% lower than the FEEMD-PSOBiLSTM combined model and the SAPSO-BiLSTM combined model, respectively. In terms of convergence speed, the FEEMD-SAPSOBiLSTM model is obviously faster than FEEMD-PSO-BiLSTM model. Therefore, in predicting short-term traffic flow, the proposed combined model improves the prediction accuracy and speed and achieves the desired prediction effect.