Recently,　researchers　have　focused　on　utilizing　given　heterogeneous　features　to　explore　obvious　discrimination　information　for　clustering.　Most　of　the　current　work　exploits　consistency　using　some　fusion　metrics,　but　the　complementarity　of　multi-view　features　is　not　well　leveraged.　In　this　paper,　we　propose　an　efficient　consistent　contrastive　representation　network　(CCR-Net)　for　multi-view　clustering,　which　provides　a　generalized　framework　for　multi-view　learning　tasks.　First,　the　proposed　model　explores　the　complementarity　by　a　designed　contrastive　fusion　module　to　learn　a　shared　fusion　weight.　Second,　the　proposed　method　utilizes　a　consistent　representation　module　to　ensure　consistency　and　obtains　a　consistent　graph.　Furthermore,　we　also　extend　the　proposed　method　to　incomplete　multi-view　scenarios.　The　designed　contrastive　fusion　module　utilizes　the　complementarity　of　multiple　views　to　fill　in　the　missing　view　graphs.　Moreover,　the　consistent　feature　representation　module　adds　a　maxpooling　layer　on　CCR-Net　to　explore　a　shared　local　structure　and　extract　a　latent　low-dimensional　embedding.　Finally,　the　proposed　method　presents　end-to-end　training　and　flexible　task　interfaces　for　multi-view　learning.　Comprehensive　evaluations　on　challenging　multi-view　tasks　demonstrate　that　the　proposed　method　achieves　outstanding　performance.