Aiming at the life prediction of servo motor rolling bearings,a long-term short-term memory network prediction method based on Pearson correlation coefficient and nuclear principal component analysis was proposed.The time and frequency domain signals of rolling bearings were extracted,the relevant features were further obtained by the moving average method,and the Pearson correlation coefficient was used to screen the highly correlated feature indexes,and several principal components of the highly correlated feature index were extracted by KPCA.The first principal component was used as input to the long-short-term memory network model to predict the remaining life of rolling bearings.The IMS bearing data set was used for verification,and the RMSE value and decisionable coefficient value of bearing life prediction were 0.054 3 and 0.989,respectively.Compared with the prediction results of long short-term memory network model and BP neural network,the results show that the proposed method has high accuracy.