With　the　explosive　growth　of　user-generated　content,　multi-view　learning　has　become　a　rapidly　growing　direction　in　pattern　recognition　and　data　analysis　areas.　Due　to　the　significant　application　value　of　multi-view　learning,　there　has　been　a　continuous　emergence　of　research　based　on　machine　learning　methods　and　traditional　deep　learning　paradigms.　The　core　challenge　in　multi-view　learning　lies　in　harnessing　both　consistent　and　complementary　information　to　forge　a　unified,　comprehensive　representation.　However,　many　multi-view　learning　tasks　are　based　on　graph-structured　data,　making　existing　methods　unable　to　effectively　mine　the　information　contained　in　the　input　multiple　data　sources.　Recently,　graph　neural　networks　(GNN)　techniques　have　been　widely　utilized　to　deal　with　non-Euclidean　data,　such　as　graphs　or　manifolds.　Thus,　it　is　essential　to　combine　the　advantages　of　the　powerful　learning　capability　of　GNN　models　and　multi-view　data.　In　this　paper,　we　aim　to　provide　a　comprehensive　survey　of　recent　research　works　on　GNN-based　multi-view　learning.　In　detail,　we　first　provide　a　taxonomy　of　GNN-based　multi-view　learning　methods　according　to　the　input　form　of　models:　multi-relation,　multi-attribute　and　mixed.　Then,　we　introduce　the　applications　of　multi-view　learning,　including　recommendation　systems,　computer　vision　and　so　on.　Moreover,　several　public　datasets　and　open-source　codes　are　introduced　for　implementation.　Finally,　we　analyze　the　challenges　of　applying　GNN　models　on　various　multi-view　learning　tasks　and　state　new　future　directions　in　this　field.