Recently,　Multi-view　Representation　Learning　(MRL)　has　drawn　immense　attentions　in　the　analysis　of　multi　-source　data　and　ubiquitously　employed　across　different　research　fields.　This　important　issue　is　designed　to　learn　a　feature　representation　with　sufficient　information　from　multiple　views.　In　this　paper,　we　propose　a　novel　Comprehensive　Multi-view　Representation　Learning　(CMRL),　which　can　fully　explore　available　information　contained　in　both　the　feature　representations　and　subspace　representations　of　multiple　views.　The　desired　feature　representation　learned　in　CMRL　profits　from　the　consistency　and　complementarity　of　multi-view　data.　Specifically,　the　complementary　information　is　mined　by　applying　the　degeneration　mapping　model　on　multiple　feature　representations,　the　consensus　information　is　explored　by　imposing　a　low-rank　tensor　constraint　on　multiple　subspace　representations.　Further,　the　objective　function　of　CMRL　is　optimized　by　an　Augmented　Lagrangian　Multiplier　(ALM)　based　algorithm.　Finally,　our　CMRL　is　evaluated　on　seven　benchmark　multi-view　datasets　and　compared　with　several　state-of-the-art　methods,　experimental　results　illustrate　the　superiority　and　effectiveness　of　the　proposed　method.　What　is　more,　we　find　that　the　proposed　method　can　also　be　successfully　applied　to　multi-view　subspace　clustering　and　achieves　promising　clustering　results.