To improve the prediction accuracy of intersection short-term traffic flow, a new CEEMDAN-PE-OSELM model is developed based on the complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN), permutation entropy(PE) and online sequential extreme learning machine(OSELM). Firstly, traffic flow historical time series are decomposed by CEEMDAN algorithm. Secondly, PE algorithm is used to recombine the IMF components obtained by CEEMDAN, and a series of restructured subsequences can be obtained, which have a significant difference in terms of complexity. Then, the OSELM prediction models are proposed for each restructured subsequence respectively, and the final results are got by adding the prediction results. Finally, a typical intersection is verified the effect and performance of the hybrid prediction model. Results show that the values of MAE, MAPE and MSE of CEEMDAN-PE-OSELM prediction model are lower than other models, and get a minimal error. The EC value of the improved model is 0.963, which is higher than that of ARIMA model (0.898) and the most close to 1. The CEEMDAN-PE-OSELM prediction model has the highest precision and best stability, and the errors decrease obviously.