Municipal　solid　waste　incineration　(MSWI)　process　has　been　one　of　the　important　emission　sources　of　dioxin　(DXN)　in　terms　of　century　posion.　Untill　now,　the　evolution　mechanism　and　real-time　detection　of　DXN　emission　concentration　are　still　unsolved　challenges.　Existing　studies　mainly　rely　on　available　data　to　build　data-driven　modeling,　and　how　to　effectively　combine　the　mechanism　of　combustion　process　for　DXN　detection　is　a　problem　that　is　not　considered.　To　solve　this　problem,　this　article　proposes　DXN　emission　modelling　method　based　on　simulation　mechanism　and　improved　linear　regression　decision　tree　(LRDT).　First,　a　numerical　simulation　model　based　on　coupling　fluid　dynamic　incinerator　code　(FLIC)　and　advanced　system　for　process　engineering　Plus　(Aspen　Plus)　software　is　used　to　obtain　virtual　mechanism　data　with　multiple　operating　conditions.　Then,　virtual　mechanism　data　is　used　to　construct　an　improved　LRDT　combustion　state　representation　variable　CO2,　CO,　and　O2　model.　Next,　a　process　mapping　model　(PMM)　based　on　multiple　input　single　output　LRDT　is　constructed　using　real　CO2,　CO,　and　O2　as　input　and　DXN　as　output.　Semi-supervised　learning　and　structural　transfer　learning　based　on　PMM　are　used　to　obtain　the　mechanism　mapping　models1　(MMM1).　Finally,　the　final　MMM2　based　on　semi-supervised　transfer　learning　is　obtained　by　the　structural　growth　learning　of　the　MMM1.　The　proposed　method　was　validated　for　industrial　application　on　a　hardware-in-loop　simulation　platform　in　the　laboratory　and　an　edge　verification　platform　at　an　MSWI　plant　in　Beijing.　The　experimental　results　show　that　the　proposed　method　and　the　developed　soft　measurement　system　can　effectively　realize　the　on-line　detection　of　DXN　emission　concentration.　©　2024　Science　Press.　All　rights　reserved.