For　accurate　prediction　of　nitrogen　oxides　(NOx)　concentration　during　the　municipal　solid　waste　incineration　(MSWI)　process,　in　this　paper,　a　prediction　modeling　method　based　on　a　sparse　regularization　stochastic　configuration　network　is　proposed.　The　method　combines　DropConnect　regularization　with　L1　regularization.　Based　on　the　L1　regularization　constraint　stochastic　configuration　network　output　weights,　DropConnect　regularization　is　applied　to　the　input　weights　to　introduce　sparsity.　A　probability　decay　strategy　based　on　network　residuals　is　designed　to　address　situations　where　the　DropConnect　fixed　drop　probability　affects　model　convergence.　Finally,　the　generated　sparse　stochastic　configuration　network　is　used　to　establish　the　model,　and　is　validated　through　experiments　with　standard　datasets　and　actual　data　from　an　MSWI　plant　in　Beijing.　The　experimental　results　prove　that　this　modeling　method　exhibits　high-precision　prediction　and　generalization　ability　while　effectively　simplifying　the　model　structure,　which　enables　accurate　prediction　of　NOx　concentration.　©　2024　by　the　authors.