Abstract: 【Objective】The price of domestic soybean is easily influenced by many factors, which is characterized by non-linearity, and it is difficult to make accurate prediction. In order to improve the prediction accuracy, an optimized EEMD-SVR integrated prediction method is proposed.【Method】For solving the problems of modal aliasing and endpoint effect in EMD decomposition, by using the optimization method of EEMD and parallel extension method, white noise was added and multiple extreme values were extended at both ends of the original sequence, and the original soybean price was decomposed into multiple IMF components. In this way, the data tended to be stabilized. The Support Vector Regression (SVR) algorithm was used to predict each component, the genetic algorithm was introduced to find the optimal solution of parameters, the prediction results of each component were re-integrated, and the market price prediction value of soybean was reconstructed.【Result】In order to test the prediction effect of the optimized combination model, a variety of models were compared, and the results showed that the prediction indicators MSE, RMSE and MAPE were improved significantly. 【Conclusion】The combined model of the optimized EEMD decomposition algorithm and support vector machine can effectively suppress the endpoint effect and modal aliasing of EMD decomposition. And the prediction effect is better than that of other traditional prediction models.