Community　detection　is　the　detection　and　revelation　of　the　communities　inherent　in　different　types　of　complex　networks,　which　can　help　people　understand　various　functions　and　hidden　rules　of　the　complex　networks　to　predict　their　future　behavior.　The　spectral　clustering　algorithm　suffers　from　the　disadvantage　of　spending　too　much　time　for　calculating　eigenvectors,　so　it　can’t　apply　in　large-scale　networks.　This　paper　puts　forward　the　overlapping　community　detection　algorithm　devised　upon　spectral　with　Fuzzy　c-means　clustering.　Firstly,　the　node　similarity　is　calculated　according　to　the　influence　of　attribute　features　on　nodes.　Secondly,　the　node　similarity　is　combined　with　the　Jaccard　similarity　to　construct　the　similarity　matrix.　Thirdly,　the　feature　decomposition　is　performed　on　the　matrix　by　using　the　DPIC　(Deflation-based　power　iteration　clustering)　method.　Finally,　the　advanced　version　of　the　traditional　Fuzzy　c-means　algorithm　can　find　the　overlapping　communities.　The　results　of　experiments　reveal　that　it　can　detect　communities　on　real　and　artificial　datasets　effectively　and　accurately.　©　Springer　Nature　Singapore　Pte　Ltd.　2019.