Asa　promising　area　in　machine　learning,　multi-view　learning　enhances　model　performance　by　integrating　data　from　various　views.　With　the　rise　of　graph　convolutional　networks,　many　studies　have　explored　incorporating　them　into　multi-view　learning　frameworks.　However,　these　methods　often　require　storing　the　entire　graph　topology,　leading　to　significant　memory　demands.　Additionally,　iterative　update　operations　in　graph　convolutions　lead　to　longer　inference　times,　making　it　difficult　to　deploy　existing　multi-view　learning　models　on　large　graphs.　To　overcome　these　challenges,　we　introduce　an　efficient　multi-view　graph　convolutional　network　via　local　aggregation　and　global　propagation.　In　the　local　aggregation　module,　we　use　a　structure-aware　matrix　for　feature　aggregation,　which　significantly　reduces　computational　complexity　compared　to　traditional　graph　convolutions.　After　that,　we　design　a　global　propagation　module　that　allows　the　model　to　be　trained　in　batches,　enabling　deployment　on　large-scale　graphs.　Finally,　we　introduce　the　attention　mechanism　into　multi-view　feature　fusion　to　more　effectively　explore　the　consistency　and　complementarity　between　views.　The　proposed　method　is　employed　to　perform　multi-view　semi-supervised　classification,　and　comprehensive　experimental　results　on　benchmark　datasets　validate　its　effectiveness.