To　deal　with　the　nonlinearity,　uncertainty　and　non-Gaussianity　of　urban　wastewater　treatment　processes,　this　paper　proposes　a　data-driven　online　self-organizing　control　method　for　dissolved　oxygen　(DO).　First,　a　correntropy-based　self-organizing　fuzzy　neural　network　(CSOFNN)　controller　is　designed.　For　CSOFNN,　its　structure　and　parameters　can　be　automatically　generated　or　pruned　based　on　the　correntropy　and　rules-contribution　indexes.　Second,　the　compensation　controller　and　parameter　adaptive　laws　are　developed　using　the　correntropy-induced　criterion,　thus　can　tackle　non-Gaussian　noise　and　reduce　the　system　uncertainty.　Third,　the　stability　of　the　proposed　control　method　is　analyzed　theoretically,　thus　ensuring　its　feasibility　in　practice.　Finally,　the　proposed　control　method　is　tested　in　the　benchmark　simulation　model　No.　1　(BSM1).　The　experimental　results　show　its　effectiveness.　©　2023　Science　Press.　All　rights　reserved.