The　complex　causes　of　sludge　bulking,　strict　system　constraints,　and　dynamic　operating　conditions　increase　the　challenges　of　controlling　wastewater　treatment　process.　To　address　this　issue,　a　data-driven　soft　constrained　model　predictive　control　(DD-SCMPC)　strategy　is　proposed,　which　can　adaptively　adjust　the　control　law　in　response　to　the　identified　fault　cause.　First,　an　intelligent　diagnosis　algorithm　is　utilized　to　identify　the　key　cause　variable　according　to　the　relative　reconstruction　contribution　of　process　variables.　Consequently,　the　priority　control　order　of　the　controlled　variables　can　be　determined　based　on　the　correlation　between　the　cause　variable　and　output　variables.　Second,　a　soft　constrained　MPC　strategy　is　designed　to　regulate　the　concentrations　of　dissolved　oxygen　and　nitrate　nitrogen　in　accordance　with　the　predetermined　control　order,　thereby　avoid　sludge　bulking　caused　by　abnormal　process　variables.　The　incorporation　of　soft　constraints　alleviates　the　strict　constraints　on　system　outputs,　enhancing　the　adaptability　of　the　controller.　Third,　a　predictive　control　barrier　function　is　designed　to　obtain　an　enlarged　attractive　domain,　ensuring　the　stability　of　the　system　under　soft　constraints.　Then,　the　feasibility　and　stability　analysis　provide　theoretical　support　for　the　application　of　DDSCMPC.　Finally,　the　effectiveness　of　the　proposed　DD-SCMPC　strategy　is　verified　on　the　benchmark　simulation　model　1.