Suspending unnecessary system or application processes in the background of the mobile phone while the user is sleeping can effectively reduce energy consumption, so it is of great significance to accurately determine whether the user is sleeping without compromising the user experience. Based on this problem, the coverage rate and wake rate are designed as new metrics. A sleep prediction model based on LSTM neural network is proposed, the LSTM neural network can handle time-series feature data and the evolution algorithm can optimize non derivable optimization targets. The parameters of the LSTM neural network are used as the optimization parameters of the differential evolution algorithm, and the comprehensive target of coverage and wake-up rates are used as the selection function. The selection function is re-evaluated in each iteration to use the mini-batch training. The experimental results show that compared with the traditional classification model, the prediction results obtained by training the LSTM neural network with evolutionary algorithm can achieve better coverage at low wake-up rate, with an average improvement of about 5%.