Aiming at the complexity of port container throughput data， a short-term hybrid prediction model of container throughput based on Optimal Variational Mode Decomposition （OVMD） and Kernel Extreme Learning Machine （KELM） was proposed. Firstly， the outliers were removed by Hampel Identifier （HI） from the original time series， and the preprocessed series was decomposed into several sub-modes with obvious characteristics by OVMD. Then， in order to improve the prediction efficiency， the decomposed sub-modes were divided into three categories according to the values of Sample Entropy （SE）： high frequency low amplitude， medium frequency medium amplitude and low frequency high amplitude. At the same time， the wavelet， Gauss and linear kernel functions carried in KELM were used to capture the trends of sub-modes with different characteristics. Finally， the final prediction result was obtained by linearly adding the prediction results of all sub- modes together. Taking the monthly container throughput data at Shenzhen Port as a sample for empirical research， the proposed model has the Mean Absolute Error （MAE） of 0.914?9， the Mean Absolute Percentage Error （MAPE） of 0.199%， the Root Mean Square Error （RMSE） of 7.886?0 and the coefficient of determination （R²） of 0.994?4. Compared with four comparison models， the proposed model has advantages in prediction accuracy and efficiency. At the same time， it overcomes the mode mixing problem in traditional Complementary Ensemble Empirical Mode Decomposition （CEEMD） and Ensemble Empirical Mode Decomposition （EEMD） as well as overfitting defect in Extreme Learning Machine （ELM）， and has practical application potential.