To　achieve　accurate　prediction　of　furnace　temperature　and　flue　gas　oxygen　content　in　municipal　solid　waste　incineration　(MSWI)　process,　a　multi-target　robust　modeling　method　based　on　improved　stochastic　configuration　network　(MRI-SCN)　is　proposed.　First,　a　parallel　method　is　designed　to　incrementally　build　SCN　hidden　layers,　which　enhances　the　diversity　of　hidden　layer　mapping　through　information　superposition　and　spanning　connection,　and　assign　hidden　layer　parameters　using　the　supervised　inequality　with　adaptive　parameter　changes.　Second,　a　matrix　elastic　net　is　established　by　using　F-norm　and　L2,　1　-norm　regularization　terms　to　sparsely　constrain　the　model　parameters　to　model　the　correlation　between　furnace　temperature　and　flue　gas　oxygen　content.　Then,　the　mixture　Laplace　distribution　is　used　as　the　prior　distribution　of　each　target　modeling　error,　and　the　output　weights　of　the　SCN　model　are　re-evaluated　by　maximum　a　posteriori　estimation　to　enhance　its　robustness.　Finally,　the　performance　of　the　proposed　modeling　method　is　tested　on　the　historical　data　of　municipal　solid　waste　incineration　process.　The　experimental　results　show　that　the　proposed　modeling　method　has　advantages　in　prediction　accuracy　and　robustness.　©　2024　Science　Press.　All　rights　reserved.