A diagnosis model of BP neural network optimized by genetic algorithm (GA) and AdaBoost algorithm was proposed,and it was used to address the problem of rolling bearing fault diagnosis.The weights and thresholds of BP network were optimized by GA (GABP),and multiple GABP was combined with AdaBoost algorithm as diagnosis model (GABP-AdaBoost).The key parameters of GABP-AdaBoost were analyzed based on UCI standard data set.The drift and weak noise in the bearing vibration signal was eliminated by least square method and exponential smoothing method,and factor analysis method was used to select the optimal timedomain parameters.Finally,GABP-AdaBoost algorithm was used to diagnose the bearing fault samples,and GABP,BP and BP-AdaBoost were used as comparison algorithms.The results of 30 repeated experiments show that the diagnostic effect of GABP-AdaBoost algorithm is more than 90%,but the diagnostic time is longer;the diagnostic effect of BP-AdaBoost algorithm is better than that of GABP and less timeconsuming;and the sensitivity of GABP-AdaBoost algorithm and BP-AdaBoost algorithm to the fluctuation of repeated diagnosis is lower