In order to improve the accuracy of short\|term wind power prediction, this paper proposes a short\|term wind power prediction model based on the ICEEMDAN\|SE\|MSGJO\|LSTM\|EC model. Firstly, the original wind power signal is decomposed using intrinsic computing expressive empirical mode decomposition with adaptive noise (ICEEMDAN) and reconstructed by adding components with similar entropy values through sample entropy calculation; Secondly, establish an multi\|strategy golden jackal optimization (MSGJO)\|long short\|term memory network (LSTM) prediction model, optimize the LSTM network parameters through the improved MSGJO, and predict various modal components; Finally, error correction (EC) is applied to the prediction results of each modal component and all modal prediction results are added to obtain the final prediction result. Taking a wind farm in Xinjiang as an example, simulation analysis was conducted using the prediction model proposed in this paper. The experimental results showed that the prediction model based on ICEEMDAN\|SE\|MSGJO\|LSTM\|EC proposed in this paper has higher prediction accuracy.