Graph　embedding　is　an　effective　yet　efficient　way　to　convert　graph　data　into　a　low　dimensional　space.　In　recent　years,　deep　learning　has　applied　on　graph　embedding　and　shown　outstanding　performance.　Adjacency　matrix　is　often　taken　as　the　storage　data　structure　of　graph.　However,　there　are　the　problems　of　insufficient　spatial　proximity　information　in　adjacency　matrix.　Therefore,　this　study　proposes　a　deep　community　detection　method　which　includes　(1)　matrix　reconstruction　method,　(2)　spatial　feature　extraction　method　and　(3)　community　detection　method.　The　original　adjacency　matrix　in　social　network　is　reconstructed　based　on　the　opinion　leader　and　nearer　neighbors　for　obtaining　spatial　proximity　matrix.　The　spatial　proximity　matrix　can　obtain　subspace　of　the　graph　which　can　help　convolution　neural　network　easily　and　quickly　extract　the　spatial　localization.　The　spatial　eigenvector　of　reconstructed　adjacency　matrix　can　be　extracted　by　an　auto-encoder　based　on　convolution　neural　network　for　the　improvement　of　modularity.　In　experiments,　four　open　datasets　of　practical　social　networks　were　selected　to　evaluate　the　proposed　method,　and　the　experimental　results　show　that　the　proposed　deep　community　detection　method　obtained　higher　modularity　than　other　deep　learning　methods.　Therefore,　the　proposed　deep　community　detection　method　can　effectively　detect　high　quality　communities　in　social　networks.