Dioxin　(DXN)　emitted　from　the　municipal　solid　waste　incineration　(MSWI)　process　is　a　persistent　pollutant　of　the　＇century　poison＇.　DXN　is　one　of　the　highly　toxic　and　persistent　pollution.　The　principal　model　of　DXN　emission　is　difficult　to　obtained　duo　to　the　complex　multi-stage　and　multi-temperature　phase＇s　physical　chemical　characteristics.　In　practical,　DXN　emission　concentration　is　off-line　measured　with　month　or　quarter　period　by　quantified　national　laboratory　with　long　lag　time　delay.　Aiming　at　these　problems,　a　new　DXN　emission　concentration　soft　measuring　method　based　on　selective　ensemble　(SEN)　kernel　learning　algorithm　is　proposed.　At　first,　candidate　kernel　parameters　and　regularization　parameters　are　given　based　on　prior　knowledge.　Then,　candidate　sub-sub-models　based　on　these　super　parameters　are　constructed.　Thirdly,　coupled　optimization　and　weighting　algorithms　are　used　to　build　SEN-sub-models.　Finally,　these　SEN-sub-models　are　selective　combined　as　final　SEN　model　by　using　optimization　and　weighting　algorithms　again.　Simulation　results　based　on　the　concrete　compression　strength　and　incineration　process　DXN　data　validate　effectiveness　of　the　proposed　approach.　©　All　Right　Reserved.