Municipal　solid　waste　incineration　(MSWI)　has　significant　advantages　in　the　treatment　of　municipal　solid　waste,　however,　the　instability　of　the　solid　waste　combustion　state　can　lead　to　a　reduction　in　the　operational　efficiency　of　the　whole　system.　In　addition,　the　judgement　of　the　MSWI　state　mainly　relies　on　the　operators＇　own　experience　to　control　the　combustion,　leading　to　the　identification　results　with　substantially　subjective　and　arbitrary.　In　order　to　address　these　challenging　problems,　a　Hierarchical-Split　residual　neural　network　is　proposed　for　MSWI　process　combustion　state　recognition,　which　is　modeled　by　collecting　combustion　images,　combining　the　experience　of　field　experts　and　the　incinerator　grate　structure,　and　classifying　the　combustion　state　into　four　categories:　normal　burning,　partial　burning,　channelling　burning,　and　smothering.　Aiming　at　the　problem　of　incomplete　image　information　extracted　by　the　traditional　single-scale　convolution,　the　Hierarchical-Split　feature　extraction　module　is　utilized　not　only　to　enhance　the　ability　of　the　model　to　extract　feature　information,　but　also　to　improve　the　recognition　accuracy　of　the　model.　Finally,　the　proposed　method　is　validated　by　taking　the　flame　data　of　a　municipal　waste　treatment　plant　in　Beijing　as　an　example.　The　experimental　results　show　that　the　method　effectively　improves　the　generalization　and　robustness,　and　has　good　recognition　accuracy.　©　2024　IEEE.