In order to improve the accuracy of pork price forecasting, a pork price ensemble prediction model is constructed, based on the decomposition ability of the complementary ensemble empirical mode decomposition (CEEMD) method and the adaptive prediction property of genetic algorithm-support vector regression (GA-SVR) model. First, for solving the complex wave characteristics of pork price, CEEMD is used to decompose pork price and obtain the Intrinsic Mode Function (IMF) sequence set. Second, permutation entropy (PE) is used to analyze the complexity of IMF sequences so as to further decompose complex sequences by the Fast Fourier Transform method (FFT). Three, grey correlation degree (GCD) is used to analyze the correlation coefficient of IMF sequences and combine the similar IMF sequences. Finally, the GA-SVR prediction model is constructed to predict the IMFs, and the prediction results of each sub-sequence are integrated to obtain the final price prediction. Taking the market pork prices in China as samples, the empirical results show that the ensemble prediction model is significantly superior to other single prediction models and decomposition ensemble prediction models in prediction accuracy and directionality indicators.