For the low diagnostic rate of traditional machine learning algorithm in bearing fault classification under variable working conditions, this paper proposed a bearing fault diagnosis method based on the combination of joint distribution adaptation ( JDA) algorithm and K-nearest neighbor ( KNN) classification algorithm. Firstly, the time domain features of bearing fault signals under different working conditions are extracted as source domain samples and target domain samples respectively, then calculating the weight of each feature by FLDA method. The feature vectors composed of features with higher weights to adapting joint distribution by JDA method, that is, the source domain samples and target domain samples are mapped to the low-dimensional potential space by kernel function, and the maximum mean discrepancy (MMD) distance is taken as the measurement standard to reduce the marginal distribution and conditional distribution differences between the source domain samples and the target domain samples. Finally, the mapped source domain and target domain samples are used as training data and test data respectively, and the model identification is implemented by KNN classifier, and the bearing fault diagnosis classification under variable conditions is achieved. Compared with the method of PCA, KPCA and TCA, through simulation analysis and experimental verification, the method proposed in this paper significantly improves the accuracy of bearing fault diagnosis under variable working conditions.