The　municipal　solid　waste　incineration　(MSWI)　process　plays　a　crucial　role　in　managing　the　risks　associated　with　waste　accumulation　and　promoting　the　sustainable　development　of　urban　environments.　However,　unstable　operation　of　the　MSWI　process　can　lead　to　excessive　pollutant　emissions,　deteriorating　air　quality,　and　adverse　impacts　on　public　health.　Flue　gas　oxygen　content　is　a　key　controlled　variable　in　the　MSWI　process,　and　its　stable　control　is　closely　linked　to　both　incineration　efficiency　and　pollutant　emissions.　Developing　a　high-precision,　interpretable　model　for　flue　gas　oxygen　content　is　essential　for　achieving　optimal　control.　However,　existing　methods　face　challenges　such　as　poor　interpretability,　low　accuracy,　and　the　complexity　of　manual　hyperparameter　tuning.　To　address　these　issues,　this　article　proposes　a　flue　gas　oxygen　content　model　based　on　a　Bayesian　optimization　(BO)　main-compensation　ensemble　modeling　algorithm.　The　model　first　utilizes　an　ensemble　TS　fuzzy　regression　tree　(EnTSFRT)　to　construct　the　main　model.　Then,　a　long　short-term　memory　network　(LSTM)　is　employed　to　build　the　compensation　model,　using　the　error　of　the　EnTSFRT　model　as　the　target　value.　The　final　output　is　obtained　through　a　weighted　combination　of　the　main　and　compensation　models.　Finally,　the　hyperparameters　of　the　main-compensation　ensemble　model　are　optimized　using　the　BO　algorithm　to　achieve　a　high　generalization　performance.　Experimental　results　based　on　real　MSWI　process　data　demonstrate　that　the　proposed　method　performs　well,　achieving　a　48.2%　reduction　in　RMSE　and　a　53.1%　reduction　in　MAE,　while　R2　increases　by　140.8%,　compared　to　the　BO-EnTSFRT　method　that　uses　only　the　main　model.