The　prevailing　method　for　handling　municipal　solid　waste　(MSW)　is　incineration,　a　critical　process　that　demands　safe,　stable,　and　eco-conscious　operation.　While　grate-typed　furnaces　offer　operational　flexibility,　they　often　generate　pollution　during　unstable　operating　conditions.　Moreover,　fluctuations　in　the　physical　and　chemical　characteristics　of　MSW　contribute　to　variable　combustion　statuses,　accelerating　internal　furnace　wear　and　ash　accumulation.　Tackling　the　challenges　of　pollution,　wear,　and　efficiency　in　the　MSW　incineration　(MSWI)　process　necessitates　the　automatic　online　recognition　of　combustion　status.　This　article　introduces　a　novel　online　recognition　method　using　deep　forest　classification　(DFC)　based　on　convolutional　multi-layer　feature　fusion.　The　method　entails　several　key　steps:　initial　collection　and　analysis　of　flame　image　modeling　data　and　construction　of　an　offline　model　utilizing　LeNet-5　and　DFC.　Here,　LeNet-5　trains　to　extract　deep　features　from　flame　images,　while　an　adaptive　selection　fusion　method　on　multi-layer　features　selects　the　most　effective　fused　deep　features.　Subsequently,　these　fused　deep　features　feed　into　DFC,　constructing　an　offline　recognition　model　for　identifying　combustion　status.　Finally,　embedding　this　recognition　system　into　an　existing　MSWI　process　data　monitoring　system　enables　online　flame　video　recognition.　Experimental　results　show　remarkable　accuracies:　93.80%　and　95.08%　for　left　and　right　grate　furnace　offline　samples,　respectively.　When　implemented　in　an　online　flame　video　recognition　platform,　it　aptly　meets　recognition　demands.