Recent　studies　highlight　the　growing　appeal　of　multi-view　learning　due　to　its　enhanced　generalization.　Semi-supervised　classification,　using　few　labeled　samples　to　classify　the　unlabeled　majority,　is　gaining　popularity　for　its　time　and　cost　efficiency,　particularly　with　high-dimensional　and　large-scale　multi-view　data.　Existing　graph-based　methods　for　multi-view　semi-supervised　classification　still　have　potential　for　improvement　in　further　enhancing　classification　accuracy.　Since　deep　random　walk　has　demonstrated　promising　performance　across　diverse　fields　and　shows　potential　for　semi-supervised　classification.　This　paper　proposes　a　deep　random　walk　inspired　multi-view　graph　convolutional　network　model　for　semi-supervised　classification　tasks　that　builds　signal　propagation　between　connected　vertices　of　the　graph　based　on　transfer　probabilities.　The　learned　representation　matrices　from　different　views　are　fused　by　an　aggregator　to　learn　appropriate　weights,　which　are　then　normalized　for　label　prediction.　The　proposed　method　partially　reduces　overfitting,　and　comprehensive　experiments　show　it　delivers　impressive　performance　compared　to　other　state-of-the-art　algorithms,　with　classification　accuracy　improving　by　more　than　5%　on　certain　test　datasets.