Aiming at the problems of strong fluctuation of wind power and low prediction accuracy, An improved logistic-t-dung beetle optimizer (LTDBO) is proposed to optimize variational mode decomposition. VMD) parameters and LTDBO algorithm optimize the hybrid short-term wind power prediction model of long short-term memory network (LSTM) hyperparameters. First, the kurtotic of the mean envelope spectrum is used as the fitness function, and LTDBO algorithm is used to optimize the decomposition layers and penalty factors of VMD. Then, the wind power sequence after data cleaning is decomposed with VMD to obtain the stationary intrinsic mode function (IMF) of different frequencies. Each IMF input was predicted by LSTM with LTDBO's hyperparameter optimization. Finally, each IMF predicted value was superposed and reconstructed to obtain the final result. The experimental results show that the LTDBO algorithm can find the optimal combination of VMD and LSTM hyperparameters, and the combined model of LTDBO-VMD-LTDBO-LSTM has better prediction accuracy and robustness in the field of wind power prediction.