Multi-view　subspace　clustering　(MVSC)　aims　to　learn　a　consistent　shared　self-representation　by　utilizing　the　consistency　and　complementarity　of　all　views,　numerous　MVSC　algorithms　have　attempted　to　obtain　the　optimal　representation　directly　from　raw　features.　However,　they　might　overlook　the　noisy　or　redundant　information　in　raw　feature　space,　resulting　in　learning　suboptimal　self-representation　and　poor　performance.　To　address　this　limitation,　an　intuitive　idea　is　introducing　deep　neural　networks　to　eliminate　the　noise　and　redundancy,　yielding　a　potential　embedding　space.　Nevertheless,　existing　deep　MVSC　methods　merely　focus　on　either　the　embeddings　or　self-expressions　to　explore　the　complementary　information,　which　hinders　subspace　learning.　In　this　paper,　we　present　a　deep　multi-view　dual　contrastive　subspace　clustering　framework　to　exploit　the　complementarity　to　learn　latent　self-representations　effectively.　Specifically,　multi-view　encoders　are　constructed　to　eliminate　noise　and　redundancy　of　the　original　features　and　capture　low-dimensional　subspace　embeddings,　from　which　the　self-representations　are　learned.　Moreover,　two　diverse　specific　fusion　methods　are　conducted　on　the　latent　subspace　embeddings　and　the　self-expressions　to　learn　shared　self-representations,　and　dual　contrastive　constraints　are　proposed　to　fully　exploit　the　complementarity　among　views.　Extensive　experiments　are　conducted　to　verify　the　effectiveness　of　the　proposed　method.