Oxygen　content　in　flue　gas　is　a　key　variable　in　the　operation　of　the　municipal　solid　waste　incineration　(MSWI)　process.　However,　the　control　performance　of　oxygen　content　could　not　be　guaranteed　due　to　the　inherent　nonlinearity　and　uncertainty　of　the　MSWI　process.　Furthermore,　frequent　operations　of　actuators　in　the　conventional　time-triggered　system　may　also　increase　the　computational　burden　and　energy　consumption.　In　this　study,　an　event-triggered　adaptive　model　predictive　control　(ET-AMPC)　scheme　is　developed　to　solve　the　above　problems.　First,　an　improved　long　short-term　memory　(ILSTM)　neural　network　is　designed　to　construct　the　prediction　model,　in　which　the　parameters　are　determined　by　the　particle　swarm　optimization　(PSO)　algorithm.　Besides,　during　the　control　process,　the　model　parameters　can　be　adjusted　adaptively　by　an　online　updating　strategy　to　tackle　the　uncertainty.　Second,　an　event-triggered　strategy　is　proposed　to　reduce　the　computational　burden　and　energy　consumption,　wherein　the　control　laws　are　updated　only　when　certain　triggering　conditions　are　satisfied.　Third,　the　gradient　descent　method　is　applied　to　obtain　the　optimized　control　law.　Moreover,　the　convergence　of　the　prediction　model　and　the　stability　of　the　whole　ET-AMPC　are　analyzed.　Finally,　the　proposed　ET-AMPC　scheme　is　evaluated　by　real　industrial　data.　The　experimental　results　demonstrate　that　the　ET-AMPC　scheme　can　achieve　satisfactory　tracking　control　performance　with　fewer　triggering　events.　Note　to　Practitioners-The　proposed　ET-AMPC　is　an　intelligent　control　scheme　of　oxygen　content　for　the　MSWI　process.　The　proposed　control　scheme　has　adequate　tracking　performance　with　varied　set-points　and　less　computational　burden.　In　practice,　practitioners　should　obtain　accurate　historical　input　and　output　data,　and　preset　event-triggered　thresholds.　The　hyperparameters　of　the　LSTM　neural　network　are　automatically　determined　by　the　PSO　algorithm,　and　the　uncertainty　can　be　captured　with　the　online　updating　strategy.　The　accurate　prediction　model　is　beneficial　for　improving　the　control　quality.　Moreover,　the　optimized　control　law　is　obtained　with　the　intuitive　gradient　descent　method.　In　addition,　the　event-triggered　strategy　can　further　reduce　unnecessary　control　optimization　operations　with　better　practicality　and　ease　of　usage.　The　superiority　and　practicability　of　the　proposed　method　are　verified　against　actual　industrial　data.