In order to improve the accuracy of wind speed prediction, this paper starts from the predictability of mining wind speed data and optimizes the performance of the prediction model, and proposes a prediction model that combines adaptive noise complete empirical mode decomposition ( CEEMDAN) and long short-term memory neural network ( LSTM). First, CEEMDAN is used to reduce the instability of the wind speed sequence and improve its predictability. Secondly, a LSTM prediction model is established for each subsequence obtained by decomposition, and an improved cuckoo search algorithm (ICS) is used to optimize the key parameters of LSTM and improve its regression performance. Then use the optimal parameter LSTM prediction model for each sub-sequence to model and predict, and superimpose to obtain the wind speed prediction result. Verified by actual measurement data, the average absolute error and average relative error of the model proposed in this paper are only 0. 82 and 0. 95. The comparative study shows that the proposed prediction model is scientific and advanced.