China has launched national carbon trading marked in 2021, and up to now, Hubei has the largest proportion of carbon trading volume, it is totally important to research the carbon trading price in Hubei. In this paper, we propose a new model for carbon price in Hubei, which is combine complete ensemble empirical mode decomposition with adaptive noise analysis (CEEMDAN) with convolutional neural network (CNN). Firstly, carbon price is decomposed by CEEMDAN into various intrinsic mode function (IMF) which are combined using sample entropy approach. Then, CNN is used to establish a point prediction model. Finally, we calculate the mean square (RMSE), mean absolute error (MAE), and coefficient of determination (R2) of the model are 0.4893, 0.6809, and 0.9754, respectively. Compare with other two models, the hybrid model proposed in this paper exhibits the best performance.