Accurate prediction of wind power plays an important role in the stable operation of the energy system and power dispatch. Due to the stochastic, intermittent, and nonlinear characteristics of wind power sequences, the use of traditional prediction and a single prediction model often suffers from low prediction accuracy and is easily interfered by noise. In order to improve the accuracy of wind power prediction, a method combining CEEMDAN decomposition technology and neural network model is proposed in this paper. Firstly, the wind power sequence is decomposed into a number of intrinsic mode components by the CEEMDAN method. The complexity of each mode component is calculated by the sample entropy value, and the different intrinsic mode components are reorganized into reconstructed subsequences based on the sample entropy values. Middle and high-frequency sequence data are predicted using the BiLSTM model, while middle and low-frequency sequence data are predicted using the TCN model. Finally, the predicted values from the different models are combined to obtain the final prediction. Through simulation experiments, the results demonstrate that the model proposed in this paper achieves the lowest values in the evaluation metrics RMSE, MAE, and SMAPE, and the highest value in the R-squared metric. The average values of these indicators are 91.4132 MW, 53.5173 MW, 22.2638 MW, and 0.9807, respectively, which are better than those of the comparison models. This indicates that the model presented in this paper has high accuracy.