Stochastic　configuration　networks　(SCNs)　have　been　widely　used　as　predictive　models　to　model　complex　nonlinear　systems　due　to　their　advantages　in　terms　of　easy-to-implement,　fast　learning　speed,　and　universal　approximation　property.　In　many　fields,　however,　the　data　generated　by　nonlinear　systems　are　often　characterized　by　dynamic　time　series　and　nonstationary,　which　result　in　the　learner　model　with　poor　generalization　performance.　This　article　presents　an　online　self-learning　stochastic　configuration　network　to　improve　the　continuous　learning　ability　of　SCNs　to　model　nonstationary　data　streams.　The　method　can　autonomously　adjust　the　parameters　and　structure　of　the　network　according　to　the　real-time　arriving　data　streams.　Specifically,　we　use　a　recursive　learning　mechanism　to　update　the　network　parameters　online　based　on　the　data　acquired　in　real　time.　In　addition,　the　structure　of　the　SCNs　is　dynamically　adjusted　by　sensitivity　analysis　and　stochastic　configuration　algorithm　to　improve　the　adaptive　and　continuous　learning　capability　of　the　network.　A　series　of　comparisons　are　carried　out　over　two　benchmark　datasets　and　one　practical　industrial　case　from　the　wastewater　treatment　process　to　verify　the　effectiveness　of　the　proposed　method.　Experimental　results　demonstrate　that　the　proposed　method　has　good　potential　for　nonstationary　data　stream　analysis.　IEEE