A squirrel search algorithm with adaptive mutation chaos (AMCSSA) is proposed in this paper to improve the learning rate of LSTM and the prediction pattern of its decay factor. This is to solve issues of the tendency of local optimal for optimized parameters and the decay of LSTM prediction efficiency during the optimization of LSTM with SSA. By calculating its time complexity, AMCSSA is proved to be able to increase the searching efficiency without increasing the complexity of the algorithm. AMCSSA adopts Chebyshev chaotic map to generate the chaotic initial population, and switches to use a nonlinear decreasing for the predator probability. The positional greedy selection strategy is used to continuously update and keep individuals of more advantages during the iteration of algorithms, then the adaptive T mutant is introduced to improve SSA's exploration capabilities in searching space. AMCSSA optimizes the parameters for the learning rate of LSTM and their decay factors, thus the predictive ability of LSTM is further improved. This is verified by experiments on the remaining useful life of rolling bearing. The results show that the prediction accuracy of LSTM optimized by AMCSSA increased by 1. 05%, 7. 61%, 8. 4%, and 7. 73%, respectively, compared to those optimized by traditional SSA, particle swarm optimization (PSO), bat algorithm (BAT), and firefly algorithm (FA). With the proposed algorithm, the number of iteration required for the optimized LSTM to complete the convergence is also reduced, so that the prediction efficiency is increased.