Municipal　solid　waste　incineration　(MSWI)　is　a　widely　used　technology　for　reducing,　harmless　and　recycling　municipal　solid　waste.　However,　dioxin　(DXN),　a　highly　toxic　pollutant　in　the　exhaust　gas　of　MSWI　process,　is　the　main　factor　that　causes　the　＂NIMBY　effect＂　in　building　incineration　power　plants.　The　existing　DXN　detection　method　by　combining　long-period　online　sampling　and　off-line　testing　cannot　meet　the　requirement　of　direct　optimization　control　of　DXN.　To　solve　this　problem,　the　prediction　model　of　DXN　emission　concentration　based　on　easy-to-measure　process　variables　has　become　a　research　hotspot.　However,　due　to　the　high　dimension　of　process　variables　and　the　complex　mechanism　of　DXN　generation,　adsorption　and　emission,　it　is　difficult　to　effectively　select　input　features　of　DXN　model.　Therefore,　this　paper　proposes　a　modeling　method　based　on　principal　component　analysis　(PCA)　and　deep　forest　regression　(DFR).　At　first,　the　dioxin　emission　characteristics　of　MSWI　process　are　described.　Then,　a　modeling　strategy　and　algorithm　including　PCA　dimension　reduction　and　DFR　modeling　are　proposed.　Finally,　the　effectiveness　of　the　proposed　method　is　verified　by　using　the　Benchmark　data　set　and　the　actual　DXN　data.