Municipal　solid　waste　incineration　(MSWI)　with　grate　technology　is　a　widely　applied　waste-to-energy　process　in　various　cities　in　China.　Meanwhile,　dioxins　(DXN)　are　emitted　at　the　stack　and　are　the　critical　environmental　indicator　for　operation　optimization　control　in　the　MSWI　process.　However,　constructing　a　high-precision　and　fast　emission　model　for　DXN　emission　operation　optimization　control　becomes　an　immediate　difficulty.　To　address　the　above　problem,　this　research　utilizes　a　novel　DXN　emission　measurement　method　using　simplified　deep　forest　regression　(DFR)　with　residual　error　fitting　(SDFR-ref).　First,　the　high-dimensional　process　variables　are　optimally　reduced　following　the　mutual　information　and　significance　test.　Then,　a　simplified　DFR　algorithm　is　established　to　infer　or　predict　the　nonlinearity　between　the　selected　process　variables　and　the　DXN　emission　concentration.　Moreover,　a　gradient　enhancement　strategy　in　terms　of　residual　error　fitting　with　a　step　factor　is　designed　to　improve　the　measurement　performance　in　the　layer-by-layer　learning　process.　Finally,　an　actual　DXN　dataset　from　2009　to　2020　of　the　MSWI　plant　in　Beijing　is　utilized　to　verify　the　SDFR-ref　method.　Comparison　experiments　demonstrate　the　superiority　of　the　proposed　method　over　other　methods　in　terms　of　measurement　accuracy　and　time　consumption.