For rolling bearing life stage identification, a small number of samples cannot be effectively identified due to the limited sample imbalance under different working conditions. To solve this problem, a multiclassifier integration of the weighted and balanced distribution adaptation method is proposed. Firstly, the training set of multiple samples in source domain is obtained by random sampling, and different initial weights are given to the samples while predicting false labels in target domain. In this way, a few samples can be trained adequately. Then, the classifiers of sample sets in the source domain are trained in the reproducing kernel Hilbert space, and the pseudo labels are optimized. Meanwhile, the weight matrix is updated iteratively. Finally, the strategy of multiclassifier ensemble is achieved. The appropriate base classifier is integrated into a strong classifier to obtain final recognition results. Combining with Fscore evaluation criteria, macroaverage and microaverage evaluation indexes are used to evaluate multiclassification tasks, which can avoid misleading recognition results by accuracy. Experiments on two data sets of rolling bearing life stages verify that the proposed method is feasible and effective.