Graph　convolutional　networks　(GCNs)　have　become　a　popular　tool　for　learning　unstructured　graph　data　due　to　their　powerful　learning　ability.　Many　researchers　have　been　interested　in　fusing　topological　structures　and　node　features　to　extract　the　correlation　information　for　classification　tasks.　However,　it　is　inadequate　to　integrate　the　embedding　from　topology　and　feature　spaces　to　gain　the　most　correlated　information.　At　the　same　time,　most　GCN-based　methods　assume　that　the　topology　graph　or　feature　graph　is　compatible　with　the　properties　of　GCNs,　but　this　is　usually　not　satisfied　since　meaningless,　missing,　or　even　unreal　edges　are　very　common　in　actual　graphs.　To　obtain　a　more　robust　and　accurate　graph　structure,　we　intend　to　construct　an　adaptive　graph　with　topology　and　feature　graphs.　We　propose　Multi-graph　Fusion　Graph　Convolutional　Networks　with　pseudo-label　supervision　(MFGCN),　which　learn　a　connected　embedding　by　fusing　the　multi-graphs　and　node　features.　We　can　obtain　the　final　node　embedding　for　semi-supervised　node　classification　by　propagating　node　features　over　multi-graphs.　Furthermore,　to　alleviate　the　problem　of　labels　missing　in　semi-supervised　classification,　a　pseudo-label　generation　mechanism　is　proposed　to　generate　more　reliable　pseudo-labels　based　on　the　similarity　of　node　features.　Extensive　experiments　on　six　benchmark　datasets　demonstrate　the　superiority　of　MFGCN　over　state-of-the-art　classification　methods.(c)　2022　Elsevier　Ltd.　All　rights　reserved.