With　the　popularity　of　surveillance　cameras　and　the　rapid　development　of　data　acquisition　technology,　multi-view　data　shows　the　traits　of　large　scale,　high　dimension　and　multi-source　heterogeneity,　which　cause　large　data　storage,　low　data　transmission　speed　and　high　algorithm　complexity,　resulting　in　a　predicament　that　“there　are　plenty　of　data　that　are　hard　to　use”.　Up　to　now,　few　domestic　and　foreign　researches　have　been　done　on　multi-view　dimensionality　reduction.　In　order　to　solve　this　problem,　this　paper　proposes　an　adaptive　multi-view　dimensionality　reduction　method　based　on　graph　embedding.　In　consideration　of　the　reconstructed　high-dimensional　data　after　the　view-angle　dimensionality　reduction,　this　method　puts　forward　an　adaptive　similarity　matrix　to　explore　the　correlation　between　dimension-reduced　data　from　different　perspectives　and　learn　the　orthogonal　projection　matrix　of　each　perspective　to　achieve　the　multi-view　dimensionality　reduction　task.　In　this　paper,　a　clustering/recognition　verification　experiment　is　performed　on　the　dimension-reduced　multi-view　data　from　multiple　data　sets.　The　experimental　results　present　that　the　proposed　method　is　better　than　other　dimensionality　reduction　methods.　©　2021,　Editorial　Department　of　CAAI　Transactions　on　Intelligent　Systems.　All　rights　reserved.