In　recent　years,　with　the　increase　of　data　scale,　multi-label　learning　with　large　scale　class　labels　has　turned　out　to　be　the　research　hotspots.　Due　to　the　huge　solution　space,　the　problem　becomes　more　complex.　Therefore,　we　propose　a　multi-label　algorithm　based　on　kernel　learning　machine　in　this　paper.　Besides,　the　Cholesky　matrix　decomposition　inverse　method　is　adopted　to　calculate　the　network　output　weight　of　the　kernel　extreme　learning　machine.　In　particular,　in　terms　of　large　matrix　inverse　problem,　the　large　matrix　is　divided　into　small　matrices　for　parallel　computation　through　using　matrix　block　method.　Compared　with　several　state-of-the-art　algorithms　on　several　benchmark　data　sets,　results　of　the　experiments　show　that　the　proposed　algorithm　makes　a　better　performance　with　large　scale　class　labels.