Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is of great significance to reduce the risk of battery use and maintain the stability of equipment. To improve the stability and accuracy of RUL prediction of the lithium-ion battery, this paper proposes an RUL prediction model based on the hybrid improved sparrow search algorithm (ISSA) and long short-term memory (LSTM) neural network. First, the outliers in the original data are decomposed using averaging. Second, a Tent chaotic map, adaptive weight, an opposition-based learning strategy, and the Cauchy variation perturbation strategy are combined to optimize the sparrow search algorithm. The parameters of the LSTM model are optimized by the improved sparrow search algorithm. Finally, the improved hybrid ISSA-LSTM model is used to complete the RUL prediction. The NASA public data set is used to verify the model. The experimental results show that the mean absolute, root mean square, and average relative percentage errors of the model are controlled within 0.016 47, 0.022 84, and 1.2048%, which can effectively improve the prediction accuracy of the RUL of lithium- ion batteries.