Graph　Neural　Networks　(GNNs)　have　emerged　as　a　dominant　tool　for　effectively　learning　from　graph　data,　leveraging　their　remarkable　learning　capabilities.　However,　many　GNN-based　techniques　assume　complete　and　accurate　graph　relations.　Unfortunately,　this　assumption　often　diverges　from　reality,　as　real-world　scenarios　frequently　exhibit　missing　and　erroneous　edges　within　graphs.　Consequently,　GNNs　that　rely　solely　on　the　original　graph　structure　inevitably　lead　to　suboptimal　results.　To　address　this　challenge,　we　propose　a　novel　approach　known　as　Multi-graph　fusion　and　Virtual　node　enhanced　Graph　Neural　Networks　(MVGNN).　Initially,　we　introduce　an　adaptive　graph　that　complements　the　original　and　feature　graphs.　This　adaptive　graph　serves　to　bridge　gaps　in　the　original　and　feature　graphs,　capturing　missing　edges　and　refining　the　graph’s　structure.　Subsequently,　we　merge　the　original,　feature,　and　adaptive　graphs　by　applying　attention　mechanisms.　In　addition,　MVGNN　strategically　designs　virtual　nodes,　which　act　as　auxiliary　elements,　changing　the　propagation　mode　between　low-weighted　edges　and　further　enhancing　the　robustness　of　the　model.　The　proposed　MVGNN　is　evaluated　on　six　benchmark　datasets,　demonstrating　its　superiority　over　existing　state-of-the-art　classification　methodologies.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Switzerland　AG　2024.