In view of the problem that the distribution differences between training samples and test samples under different working conditions cannot effectively identify the life stage of rolling bearings，an improved method for identifying the life stage of rolling bearings based on balanced distribution is proposed. Firstly，non-repetitive uniform random sampling is used to conduct multiple uniform random sampling of inter-class samples in source domain，the training set of multi-sample in source domain is obtained to reduce the influence of sample selection in source domain on target domain prediction label. Furthermore，the weights of edge distribution and conditional distribution are dynamically adjusted in reproducing kernel Hilbert space by using equilibrium factor μ，the weights of edge distribution and conditional distribution are continuously optimized by iteration. In order to reduce the maximum mean difference between the two domains，pseudo-labels in the target domain are transformed into pseudo-labels. Finally，multiple classifiers are constructed by using the mapping matrices of the source domain data sets，the final recognition results of the target domain samples are obtained by consistency discrimination. Experiments on two sets of data sets of rolling bearing life stages show that the proposed method is feasible and effective.