Stochastic　configuration　networks　(SCNs)　have　been　widely　used　for　modeling　complex　industrial　process　due　to　their　rapid　learning　speed,　ease　of　implementation,　and　universal　approximation　capability.　For　modeling　water　quality　parameters　in　wastewater　treatment　processes　(WWTP),　however,　multiple　complex　tasks　are　often　required　to　be　modelled　simultaneously.　In　this　paper,　a　multi-task　stochastic　configuration　network　with　autonomous　linking　characteristic　is　proposed　to　further　develop　the　modeling　capability　of　SCNs　to　deal　with　multi-tasks　and　achieve　simultaneous　measurement　of　multiple　critical　water　quality　parameters　in　the　WWTP.　The　method　can　autonomously　construct　corresponding　common　nodes　and　proprietary　nodes　according　to　the　distribution　characteristics　of　different　tasks　to　model　the　shared　and　private　information　among　these　tasks.　Specifically,　the　relevant　information　between　these　tasks　is　explored　by　constructing　common　nodes;　then　personalized　approximation　of　each　task　is　achieved　by　constructing　proprietary　nodes　for　different　tasks,　thus　improving　the　overall　modeling　performance　of　the　model.　A　series　of　benchmark　experiments　and　an　industrial　case　from　WWTP　are　carried　out　to　verify　the　superiority　of　the　proposed　method.　Experimental　results　demonstrate　that　our　proposed　method　has　a　promising　potential　for　multi-task　data　modeling.