Graph　convolutional　network　has　emerged　as　a　focal　point　in　machine　learning　because　of　its　robust　graph　processing　capability.　Most　existing　graph　convolutional　network-based　approaches　are　designed　for　single-　view　data,　yet　in　many　practical　scenarios,　data　is　represented　through　multiple　views.　Moreover,　due　to　the　complexity　of　multiple　views,　normal　graph　generation　methods　cannot　mitigate　redundancy　to　generate　a　high　quality　graph.　Although　the　ability　of　graph　convolutional　network　is　undeniable,　the　quality　of　graph　directly　affects　its　performance.　To　tackle　the　aforementioned　challenges,　this　paper　proposes　a　multi-scale　graph　generation　deep　learning　framework,　called　multi-scale　semi-supervised　graph　generation　based　multi-　view　classification,　consisting　of　two　modules:　edge　sampling　and　path　sampling.　The　former　aims　to　generate　an　adjacency　graph　by　selecting　edges　based　on　the　maximum　likelihood　among　graphs　from　different　views.　Meanwhile,　the　latter　seeks　to　construct　an　adjacency　graph　according　to　the　characteristics　of　paths　within　the　graphs.　Finally,　the　statistical　technique　is　employed　to　extract　commonality　and　generate　a　fused　graph.　Extensive　experimental　results　robustly　demonstrate　the　superior　performance　of　our　proposed　framework,　compared　to　other　state-of-the-art　multi-view　semi-supervised　approaches.