Graph　convolutional　network　has　been　extensively　employed　in　semi-supervised　classification　tasks.　Although　some　studies　have　attempted　to　leverage　graph　convolutional　networks　to　explore　multi-view　data,　they　mostly　consider　the　fusion　of　feature　and　topology　individually,　leading　to　the　underutilization　of　the　consistency　and　complementarity　of　multi-view　data.　In　this　paper,　we　propose　an　end-to-end　joint　fusion　framework　that　aims　to　simultaneously　conduct　a　consistent　feature　integration　and　an　adaptive　topology　adjustment.　Specifically,　to　capture　the　feature　consistency,　we　construct　a　deep　matrix　decomposition　module,　which　maps　data　from　different　views　onto　a　feature　space　obtaining　a　consistent　feature　representation.　Moreover,　we　design　a　more　flexible　graph　convolution　that　allows　to　adaptively　learn　a　more　robust　topology.　A　dynamic　topology　can　greatly　reduce　the　influence　of　unreliable　information,　which　acquires　a　more　adaptive　representation.　As　a　result,　our　method　jointly　designs　an　effective　feature　fusion　module　and　a　topology　adjustment　module,　and　lets　these　two　modules　mutually　enhance　each　other.　It　takes　full　advantage　of　the　consistency　and　complementarity　to　better　capture　the　more　intrinsic　information.　The　experimental　results　indicate　that　our　method　surpasses　state-of-the-art　semi-supervised　classification　methods.　©　2023　Elsevier　Ltd