Multi-view　subspace　clustering　(MVSC),　which　leverages　comprehensive　information　from　multiple　views　to　effectively　reveal　the　intrinsic　relationships　among　instances,　has　garnered　significant　research　interest.　However,　previous　MVSC　research　focuses　on　exploring　the　cross-view　consistent　information　only　in　the　instance　representation　hierarchy　or　affinity　relationship　hierarchy,　which　prevents　a　joint　investigation　of　the　multi-view　consistency　in　multiple　hierarchies.　To　this　end,　we　propose　a　Triple-gRanularity　contrastive　learning　framework　for　deep　mUlti-view　Subspace　clusTering　(TRUST),　which　benefits　from　the　comprehensive　discovery　of　valuable　information　from　three　hierarchies,　including　the　instance,　specific-affinity　relationship,　and　consensus-affinity　relationship.　Specifically,　we　first　use　multiple　view-specific　autoencoders　to　extract　noise-robust　instance　representations,　which　are　then　respectively　input　into　the　MLP　model　and　self-representation　model　to　obtain　high-level　instance　representations　and　view-specific　affinity　matrices.　Then,　the　instance　and　specific-affinity　relationship　contrastive　regularization　terms　are　separately　imposed　on　the　high-level　instance　representations　and　view　specific-affinity　matrices,　ensuring　the　cross-view　consistency　can　be　found　from　the　instance　representations　to　the　view-specific　affinity　matrices.　Furthermore,　multiple　view-specific　affinity　matrices　are　fused　into　a　consensus　one　associated　with　the　consensus-affinity　relationship　contrastive　constraint,　which　embeds　the　local　structural　relationship　of　high-level　instance　representations　into　the　consensus　affinity　matrix.　Extensive　experiments　on　various　datasets　demonstrate　that　our　method　is　more　effective　when　compared　with　other　state-of-art　methods.