One　of　the　main　reasons　leading　to　＇not　in　my　backyard　(NIMBY)＇　of　municipal　solid　waste　incineration　(MSWI)　plant　construction　is　dioxin　(DXN)　emission　from　such　process,　which　is　a　highly　toxic　substance　to　the　ecological　environment.　In　practical　industrial　process,　the　DXN　emission　concentration　is　detected　by　off-line.　It　is　difficult　to　meet　the　requirements　of　optimal　control.　Aim　at　the　above　problem,　a　new　DXN　emission　concentration　soft　measurement　approach　based　on　multi-source　latent　feature　selective　ensemble　(SEN)　modeling　is　proposed.　Firstly,　MSWI　process　is　divided　into　different　subsystems　according　to　industrial　processes.　Principal　component　analysis　(PCA)　was　used　to　extract　their　latent　features.　Primary　selection　of　these　features　is　made　based　on　empirical　pre-set　threshold　of　contribution　rate.　Then,　mutual　information　(MI)　is　used　to　measure　the　correlation　between　these　primary　selected　features　and　DXN.　The　upper　and　lower　limits　and　thresholds　for　re-selected　feature　are　adaptively　determined.　Finally,　based　on　the　re-selected　feature,　the　least　squares-support　vector　machine　(LS-SVM)　algorithm　with　hyper-parameter　adaptive　selection　mechanism　is　used　to　construct　sub-models.　A　strategy　based　on　branch　and　bound　(BB)　and　prediction　error　information　entropy　weighting　algorithm　is　used　to　select　sub-model　and　calculate　the　weight　coefficient.　Thus,　an　SEN　soft　sensing　model　is　obtained.　The　proposed　method　is　verified　by　using　DXN　detection　data　of　MSWI　process　in　Beijing.　Copyright　©2022　Acta　Automatica　Sinica.　All　rights　reserved.