Deep　multi-view　representation　learning　focuses　on　training　a　unified　low-dimensional　representation　for　data　with　multiple　sources　or　modalities.　With　the　rapidly　growing　attention　of　graph　neural　networks,　more　and　more　researchers　have　introduced　various　graph　models　into　multi-view　learning.　Although　considerable　achievements　have　been　made,　most　existing　methods　usually　propagate　information　in　a　single　view　and　fuse　multiple　information　only　from　the　perspective　of　attributes　or　relationships.　To　solve　the　aforementioned　problems,　we　propose　an　efficient　model　termed　Dual　Fusion-Propagation　Graph　Neural　Network　(DFP-GNN)　and　apply　it　to　deep　multi-view　clustering　tasks.　The　proposed　method　is　designed　with　three　submodules　and　has　the　following　merits:　a)　The　proposed　view-specific　and　cross-view　propagation　modules　can　capture　the　consistency　and　complementarity　information　among　multiple　views;　b)　The　designed　fusion　module　performs　multi-view　information　fusion　with　the　attributes　of　nodes　and　the　relationships　among　them　simultaneously.　Experiments　on　popular　databases　show　that　DFP-GNN　achieves　significant　results　compared　with　several　state-of-the-art　algorithms.　IEEE