The randomicity and fluctuation of the wind speed will influence the precision of the forecast. To improve the precision of wind speed forecast, this paper presents a new method of combined wind speed forecast based on the second decomposition and weighted average. First, the ICEEMDAN decomposition method is used to get different sub-sequences, and then the fuzzy entropy is used to judge the degree of confusion of the sub-sequences. In this paper, the ARIMA model is used to predict the minimum fuzzy entropy. And the other subsequences are decomposed by BPNN, VMD and predicted by NAR and BP neural network with suitable weighting ratio for weighted average and PSO-LSTM neural network respectively, and ultimately all the predicted values are superimposed to get the final prediction. Experiments were conducted using three datasets and eight comparison models to verify the validity of this model. The prediction analysis was carried out using the actual measured data of a wind farm in Inner Mongolia, and the results indicated that (1) using fuzzy entropy can effectively improve the prediction precision; (2) the prediction accuracy of the combined prediction method of neural network based on secondary decomposition was greatly improved and the prediction results were more reliable; (3) Decompose one of the subsequences with VMD, predict it with NAR and BP neural network, and choose appropriate weight ratio for weighted average prediction will achieve better prediction results; (4) the root mean square error (RMSE) of the hybrid model on the three wind speed 1 datasets were 0.28777, 0.22786 and 0.17128, which are lower than the comparison values of other models. So, it is workable to use this hybrid model in wind speed prediction.