Multi-label　classification　learning　provides　a　multi-dimensional　perspective　for　polysemic　object,　and　becomes　a　new　research　hotspot　in　machine　learning　in　recent　years.　In　the　big　data　environment,　it　is　urgent　to　obtain　a　fast　and　efficient　multi-label　classification　algorithm.　Kernel　extreme　learning　machine　was　applied　to　multi-label　classification　problem　(ML-KELM)　in　this　paper,　so　the　iterative　learning　operations　can　be　avoided.　Meanwhile,　a　dynamic,　self-adaptive　threshold　function　was　designed　to　solve　the　transformation　from　ML-KELM　network＇s　real-value　outputs　to　binary　multi-label　vector.　ML-KELM　has　the　least　square　optimal　solution　of　ELM,　and　less　parameters　that　needs　adjustment,　stable　running,　faster　convergence　speed　and　better　generalization　performance.　Extensive　multi-label　classification　experiments　were　conducted　on　data　sets　of　different　scale.　Comparison　results　show　that　ML-KELM　outperformance　in　large　scale　dataset　with　high　dimension　instance　feature.　©　2017　Elsevier　B.V.