With the gradual improvement of the national carbon emission trading market, the accurate forecasting of the carbon emission trading price will help to establish a more stable market environment and greatly reduce the risk of participants. To address the current problems such as the difficulty of carbon trading price forecasting and the imperfection of the existing secondary decomposition-aggregation strategy, this paper proposed a new forecasting strategy. This strategy was based on variational mode decomposition (VMD) and empirical wavelet transform (EWT). In addition, central frequency (CF) and Lempel-Ziv complexity calculations were used in this strategy as the basis for determining the number of decomposition levels. Meanwhile, sample entropy (SE) was used as the basis for data reconstruction. And the reconstructed new data was served as input data for secondary decomposition. Then, this strategy used long short-term memory (LSTM) and temporal convolutional network (TCN) as forecasting models. And it was combined with marine predator algorithm (MPA) in the model for parameter optimization. The experimental results show that the V-LSTM-E-LSTM model and the V-TCN-E-TCN model not only achieve the best results in the short-term and long-term forecasting of the carbon trading price in Hubei, but also achieve high accuracy in the other four regional carbon emission allowance trading markets. However, in the national carbon emissions trading market with a relatively short establishment time, the V-TCN-E-TCN model performs better in short-term forecasting, and the V-TCN-E-LSTM model is more effective in long-term forecasting.