In　view　of　the　problem　of　random　and　continuous　data　missing　in　municipal　solid　waste　incineration　(MSWI)　process,　a　filling　method　of　missing　data　based　on　expert　experiences　and　ensemble　model　of　reduced　features　was　proposed.　First,　according　to　the　lack　conditions　of　process　data,　the　missing　data　were　divided　into　three　types,　i.　e.,　missing　with　random　distribution,　time　dimension　and　feature　dimension.　Then,　the　former　two　ones　were　filled　based　on　expert　experience,　and　the　distribution　similarity　and　mutual　information　(MI)　correlation　were　used　to　select　the　modeling　data　and　reduce　the　input　feature　for　the　third　type　one.　Sub鄄models　with　complementary　characteristics　based　on　random　forest　(RF),　gradient　boosting　decision　tree　(GBDT)　and　back　propagation　neural　network　(BPNN)　were　used　to　predict　the　preliminary　missing　values.　Furthermore,　the　fusion　model　based　on　Bayesian　linear　regression　(BLR)　was　used　to　obtain　the　final　data　filling　values.　Finally,　a　soft鄄sensor　model　of　dioxin　(DXN)　emission　concentration　based　on　deep　forest　regression　with　cross鄄layer　full　connection　(DFR鄄clfc)　was　established　to　verify　the　filling　effect.　Results　show　that　the　proposed　method　improves　the　data　quality　of　MSWI　process.　©　2023　Beijing　University　of　Technology.　All　rights　reserved.