To　accurately　predict　the　furnace　temperature　and　flue　gas　oxygen　content　of　the　municipal　solid　waste　incineration　(MSWI)　process　under　variable　operating　conditions,　a　data-driven　multitarget　online　predictive　modeling　method　based　on　an　improved　stochastic　configuration　network　(SCN)　is　proposed　in　this　article.　This　method　configures　the　input　weights　and　biases　of　the　new　hidden　layer　nodes　of　the　SCN　through　a　competitive　guidance　strategy.　The　model　output　weights　are　sparsely　constrained　using　a　matrix　elastic　net,　the　modeling　accuracy　is　improved　by　using　the　correlation　between　the　furnace　temperature　and　flue　gas　oxygen　content.　On　this　basis,　the　output　weights　of　the　model　are　recursively　updated　using　a　sparse　constrained　direction　forgetting　algorithm　to　improve　the　online　prediction　accuracy.　Finally,　the　performance　of　the　proposed　method　is　validated　using　historical　data　from　the　MSWI　process.　The　experimental　results　show　that　the　proposed　multitarget　prediction　model　is　adaptive　and　can　accurately　predict　the　trends　of　furnace　temperature　and　flue　gas　oxygen　content　under　variable　operating　conditions.