Recent　years　have　seen　the　resurgence　of　self-supervised　learning　in　visual　representation　thanks　to　Contrastive　Learning　and　Masked　Image　Modeling.　The　existing　self-supervised　methods　for　skeleton-based　action　recognition　typically　learn　feature　invariance　of　the　data　only　through　contrastive　learning.　In　this　paper,　we　propose　a　contrast　learning　method　combined　with　a　temporal-masking　mechanism　of　skeleton　sequences　to　encourage　the　network　able　to　learn　action　representations　other　than　feature　invariance,　e.g.,　occlusion　invariance,　by　implicitly　reconstructing　the　masked　sequences.　However,　the　direct　masking　mechanism　destroys　the　feature　consistency　of　the　samples,　for　which　we　propose　Supervised　Positive　Sample　Mining　and　self-attention　module　for　embeddings　to　improve　the　generalization　of　the　model.　First　of　all,　supervised　contrastive　learning　can　improve　the　robustness　of　models　using　prior　knowledge　of　labels.　Secondly,　to　avoid　excessive　masking　mechanism　that　hinders　the　model　from　learning　the　correct　occlusion　invariance,　a　self-attention　mechanism　is　necessary,　which　further　discriminate　the　distance　for　each　action　class　in　the　feature　space.　The　results　of　various　experimental　protocols　on　NTU　60,　NTU　120,　PKU-MMD　datasets　demonstrate　the　advantages　of　our　method　and　that　our　method　outperforms　the　existing　state-of-the-art　contrastive　methods.　Code　is　available　at　https://github.com/ZZFCV/SASOiCLR.