Because　of　coupling,　nonlinearity,　and　uncertainty　in　a　municipal　solid　waste　incineration　(MSWI)　process,　a　suitable　multivariable　controller　is　difficult　to　establish　under　strong　disturbance.　Additionally,　the　problems　of　reducing　mechanical　wear　and　energy　consumption　in　the　control　process　should　also　be　considered.　To　solve　these　problems,　an　event-triggered　fuzzy　neural　multivariable　controller　is　proposed　in　this　paper.　First,　the　MSWI　object　model　based　on　the　multiinput　multioutput　Takagi-Sugeno　fuzzy　neural　network　is　established　using　a　data-driven　method.　Second,　a　fuzzy　neural　multivariable　controller　is　designed　to　control　the　furnace　temperature　and　flue　gas　oxygen　content　synchronously　under　external　disturbance.　Third,　an　event-triggered　mechanism　is　constructed　to　update　the　control　rate　online　while　ensuring　control　effects.　Then,　the　stability　of　the　proposed　control　strategy　is　proven　through　the　Lyapunov　II　theorem　to　guide　its　practical　application.　Finally,　the　effectiveness　of　the　controller　is　verified　using　the　real　industrial　data　of　an　MSWI　factory　in　Beijing,　China.　The　experimental　results　show　that　the　proposed　control　strategy　greatly　improves　the　control　efficiency,　reduces　energy　consumption　by　66.23%,　and　improves　the　multivariable　tracking　control　accuracy.