Considering　the　accuracy,　generalization　ability,　stability,　and　training　efficiency　of　a　furnace　temperature　model　in　the　process　of　municipal　solid　waste　incineration,　a　heterogeneous　feature　ensemble　modeling　method　for　furnace　temperature　is　proposed　in　this　paper.　First,　heterogeneous　features　are　generated　according　to　the　operation　mechanism　of　the　waste　incineration　process,　and　the　training　subset　of　the　furnace　temperature-　and　grate　temperature-based　model　is　determined　from　the　historical　data　of　this　process.　Second,　the　base　model　pools　of　furnace　temperature　and　grate　temperature　are　constructed　by　a　regularized　stochastic　configuration　network,　and　a　set　of　optimal　base　models　are　retained　by　selective　base　model　technology.　Then,　a　negative　correlation　learning　strategy　is　employed　to　establish　a　simultaneous　training　ensemble　model　of　furnace　temperature,　and　a　regularized　stochastic　configuration　network　is　used　to　establish　a　secondary　training　ensemble　model　of　furnace　temperature.　The　final　output　of　the　furnace　temperature　is　obtained　by　the　average　value　of　the　output　of　the　above　two　ensemble　models.　Finally,　a　comparative　experiment　is　carried　out　using　the　historical　data　of　a　waste　incineration　plant.　The　results　show　that　the　furnace　temperature　model　established　in　this　paper　has　advantages　in　accuracy,　generalization　ability,　stability,　and　training　efficiency.　It　can　be　applied　to　the　field　of　furnace　temperature　prediction　and　control　in　the　waste　incineration　process.