Social　Internet　of　Things　is　the　fusion　carrier　of　social　network　and　Internet　of　Things.　In　the　social　Internet　of　Things,　millions　of　different　intelligent　objects　connect　and　communicate　with　each　other,　and　social　data　emerge　rapidly,　which　puts　forward　higher　requirements　for　the　rapid　dissemination　of　valuable　information.　As　an　important　means　of　information　retrieval,　the　research　and　application　of　multi-label　classification　technology　in　social　Internet　of　Things　environment　is　relatively　few.　Characterized　by　multi-labels　cognitive　learning,　fast　response,　concept　drifting　and　huge　solution　space,　the　multi-label　learning　becomes　more　complicated　under　data　stream　environment.　To　overcome　the　problems　above,　this　paper　proposed　a　multi-label　algorithm　based　on　kernel　extreme　learning　machine.　In　the　training　phase,　both　Cholesky　matrix　decomposition　inverse　method　and　matrix　block　method　were　deployed　on　the　Spark　platform　to　solve　the　inverse　problem　of　large　matrix,　thus　improving　the　modeling　efficiency.　In　addition,　incremental　learning　of　the　model　was　realized　from　the　two　aspects　of　example　increment　and　class　increment.　The　online　sequential　extreme　learning　machine　is　improved　to　adjust　the　network　weights　and　realize　incremental　updating　of　input　dimension.　Meanwhile,　the　output　layer　nodes　were　divided　and　assembled　in　the　way　of　＇concept　group,＇　which　made　the　single　classifier　model　structure　scalable.　And　the　label　incremental　learning　is　realized　in　the　output　dimension.　Experimental　results　on　large-scale　datasets　and　practical　datasets　under　stream　environment　demonstrate　that　the　proposed　method　provides　an　efficient　solution　to　multi-label　classification.　©　2013　IEEE.