To　improve　the　generalizability　and　robustness　of　stochastic　configuration　networks　(SCNs),　this　paper　proposes　a　robust　modeling　method　based　on　information　superposition　and　mixture　correntropy.　First,　the　mapping　information　of　the　(sigmoid)　activation　function　and　its　derivative　function　is　superimposed,　and　the　hidden　layer　parameters　are　randomly　assigned　through　a　supervisory　mechanism　to　improve　the　diversity　of　the　hidden　layer　mapping.　Second,　mixture　correntropy　is　used　to　construct　a　robust　loss　function,　and　different　Gaussian　kernels　are　used　to　measure　the　contribution　of　training　samples　to　suppress　the　negative　impact　of　data　noise　on　the　accuracy　of　the　model.　Finally,　the　performance　of　the　proposed　modeling　method　is　tested　on　functional　approximation,　four　benchmark　datasets,　and　historical　data　from　the　municipal　solid　waste　incineration　process.　The　experimental　results　show　that　the　modeling　method　proposed　in　this　paper　has　advantages　in　terms　of　generalizability　and　robustness.