This paper presents a method of bearing remaining useful life prediction based on KPCA and long short memory neural network (LSTM) with dropout. Firstly, 14 timedomain indexes of vibration signal, such as effective value, maximum value, peakpeak value and kurtosis, are extracted. KPCA method is used to fuse the timedomain characteristic indexes of bearing vibration signal and extract the first principal component to evaluate the degradation of bearing performance, and multiple principal components that meet the requirements are used as the input of the prediction model. Then an improved LSTM prediction model based on dropout strategy is established finally, the bearing data are used to verify the proposed method. The results show that the proposed method can effectively predict the remaining useful life of the bearing, and has a good prediction effect. The prediction accuracy reaches 9592%.