Multi-view　learning　has　demonstrated　strong　potential　in　processing　data　from　different　sources　or　viewpoints.　Despite　the　significant　progress　made　by　Multi-view　Graph　Neural　Networks　(MvGNNs)　in　exploiting　graph　structures,　features,　and　representations,　existing　research　generally　lacks　architectures　specifically　designed　for　the　intrinsic　properties　of　multi-view　data.　This　leads　to　models　that　still　have　deficiencies　in　fully　utilizing　consistent　and　complementary　information　in　multi-view　data.　Most　of　current　research　tends　to　simply　extend　the　single-view　GNN　framework　to　multi-view　data,　lacking　in-depth　strategies　to　handle　and　leverage　the　unique　properties　of　these　data.　To　address　this　issue,　we　propose　a　simple　yet　effective　MvGNN　framework　called　Multi-view　Representation　Learning　with　Decoupled　private　and　shared　Propagation　(MvRL-DP).　This　framework　enables　multi-view　data　to　be　effectively　processed　as　a　whole　by　alternating　private　and　shared　operations　to　integrate　cross-view　information.　In　addition,　to　address　possible　inconsistencies　between　views,　we　present　a　discriminative　loss　that　promotes　class　separability　and　prevents　the　model　from　being　misled　by　noise　hidden　in　multi-view　data.　Experiments　demonstrate　that　the　proposed　framework　is　superior　to　current　state-of-the-art　methods　in　the　multi-view　semi-supervised　classification　task.