In this paper, we address skeleton-based action recognition under the self-supervised setting. We propose a novel framework Bayesian Contrastive Learning with Manifold Regularization (BCLR). In Bayesian contrastive learning, we employ Monte Carlo Dropout sampling on the adjacency matrix of the skeleton data to obtain positive/negative samples for model robustness. Extensive experiments on NTU RGB+D and PKUMMD show that the proposed method achieves remarkable action recognition performance.