Describing　an　object　from　multiple　perspectives　often　leads　to　incomplete　data　representation.　Consequently,　learning　consistent　representations　for　missing　data　from　multiple　views　has　emerged　as　a　key　focus　in　the　realm　of　Incomplete　Multi-view　Representation　Learning　(IMRL).　In　recent　years,　various　strategies,　such　as　subspace　learning,　matrix　decomposition,　and　deep　learning,　have　been　harnessed　to　develop　numerous　IMRL　methods.　In　this　article,　our　primary　research　revolves　around　IMRL,　with　a　particular　emphasis　on　addressing　two　main　challenges.　Firstly,　we　delve　into　the　effective　integration　of　intra-view　similarity　and　contextual　structure　into　a　unified　framework.　Secondly,　we　explore　the　effective　facilitation　of　information　exchange　and　fusion　across　multiple　views.　To　tackle　these　issues,　we　propose　a　deep　learning　approach　known　as　Structural　Contrastive　Auto-Encoder　(SCAE)　to　solve　the　challenges　of　IMRL.　SCAE　comprises　two　major　components:　intra-view　structural　representation　learning　and　inter-view　contrastive　representation　learning.　The　former　involves　capturing　intra-view　similarity　by　minimizing　the　Dirichlet　energy　of　the　feature　matrix,　while　also　applying　spatial　dispersion　regularization　to　capture　intra-view　contextual　structure.　The　latter　encourages　maximizing　the　mutual　information　of　inter-view　representations,　facilitating　information　exchange　and　fusion　across　views.　Experimental　results　demonstrate　the　efficacy　of　our　approach　in　significantly　enhancing　model　accuracy　and　robustly　addressing　IMRL　problems.　The　code　is　available　at　https://github.com/limengran98/SCAE.