The　municipal　solid　waste　incineration　(MSWI)　process　usually　relies　on　operating　experts　to　observe　the　flame　inside　furnace　for　recognizing　the　combustion　states.　Then,　by　combining　the　experts＇own　experience　to　modify　the　control　strategy　to　maintain　the　stable　combustion.　Thus,　this　manual　mode　has　disadvantages　of　low　intelligence　and　the　subjectivity　and　randomness　recognition　results.　The　traditional　methods　are　difficult　to　apply　to　the　MSWI　process,　which　has　the　characteristics　of　strong　pollution,　multiple　noise,　and　scarcity　of　samples　under　abnormal　conditions.　To　solve　the　above　problems,　a　combustion　states　recognition　method　of　MSWI　process　based　on　mixed　data　enhancement　is　proposed.　Firstly,　combustion　states　are　labeled　by　combining　the　experience　of　domain　experts　and　the　design　structure　of　furnace　grate.　Next,　a　deep　convolutional　generative　adversarial　network　(DCGAN)　consisting　of　two　levels　of　coarse　and　fine-tuning　was　designed　to　acquire　multi-situation　flame　images.　Then,　the　Fréchet　inception　distance　(FID)　is　used　to　adaptively　select　generated　samples.　Finally,　the　sample　features　are　enriched　at　the　second　time　by　using　non-generative　data　enhancement　strategy,　and　a　convolutional　neural　network　is　constructed　based　on　the　mixed　enhanced　data　to　recognize　the　combustion　state.　Experiments　based　on　actual　operating　data　of　a　MSWI　plant　show　that　this　method　effectively　improves　the　generalization　and　robustness　of　the　recognition　network　and　has　good　recognition　accuracy.　©　2024　Science　Press.　All　rights　reserved.