Aiming at the non-stationary capacity degradation trend caused by the capacity recovery during the use of lithium batteries, which makes the prediction accuracy of the model vulnerable to interference, a long short-term memory network (LSTM) prediction method of lithium battery remaining useful life based on variational mode decomposition (VMD) and bayesian optimization (Bo) is proposed. Firstly, the capacity data of lithium battery is decomposed by variational modal decomposition, and a finite number of modal components are obtained; Then the decomposed components are denoised and reconstructed; Finally, the Bayesian optimized long and short-term memory neural network algorithm is used to predict the service life of the processed data, and the final prediction result of remaining useful life (RUL) of lithium battery is obtained. Through the experiment on the lithium-ion battery data set of CALCE center, the proposed VMD-BO-LSTM lithium battery combination prediction model has high prediction accuracy and stability, and the average value of the root mean square error of the battery used in the experiment is less than 7%, and is better than other prediction models.