With　the　growing　diversity　of　data　sources,　multi-view　learning　methods　have　attracted　considerable　attention.　Among　these,　by　modeling　the　multi-view　data　as　multi-view　graphs,　multi-view　Graph　Neural　Networks　(GNNs)　have　shown　encouraging　performance　on　various　multi-view　learning　tasks.　The　message　passing　is　the　critical　mechanism　empowering　GNNs　with　superior　capacity　to　process　complex　graph　data.　However,　most　multi-view　GNNs　are　designed　on　the　well-established　overall　framework,　overlooking　the　intrinsic　challenges　of　the　message　passing　on　multi-view　scenarios.　To　clarify　this,　we　first　revisit　the　message　passing　mechanism　from　a　Laplacian　smoothing　perspective,　revealing　the　key　to　designing　a　multi-view　message　passing.　Following　the　analysis,　in　this　paper,　we　propose　an　enhanced　GNN　framework　termed　Confluent　Graph　Neural　Networks　(CGNN),　with　Cross-view　Confulent　Message　Pssing　(CCMP)　tailored　for　multi-view　learning.　Inspired　by　the　optimization　of　an　improved　multi-view　Laplacian　smoothing　problem,　CCMP　contains　three　sub-modules　that　enable　the　interaction　between　graph　structures　and　consistent　representations,　which　makes　it　aware　of　consistency　and　complementarity　information　across　views.　Extensive　experiments　on　four　types　of　data　including　multi-modality　data　demonstrate　that　our　proposed　model　exhibits　superior　effectiveness　and　robustness.　The　code　is　available　at　https://github.com/shumanzhuang/CGNN.　©　2024　ACM.