Decision-making　is　essential　to　utilize　the　operation　information　of　wastewater　treatment　process　(WWTP)　to　provide　the　inhibition　strategy　for　sludge　bulking.　However,　since　majority　of　decision-making　models　focus　solely　on　knowledge　or　data　resources,　avoiding　the　interrelations　and　dependencies　between　the　operation　information,　these　models　are　difficult　to　obtain　comprehensive　and　precise　solutions.　Thus,　to　solve　this　problem,　a　knowledge-aided　and　data-driven　fuzzy　decision-making　(KD-FDM)　model　is　designed　for　sludge　bulking.　First,　a　recursive　reconstruction　contribution　(RRC)　method　is　proposed　to　analyze　the　operation　data　to　diagnose　the　fault　of　sludge　bulking.　Then,　the　fault　information　can　be　recorded　as　valid　knowledge　to　assist　in　decision-making.　Second,　a　knowledge　internalization　mechanism　(KIM)　is　developed　to　make　use　of　the　knowledge　from　the　results　of　RRC　and　the　expert　experience　of　sludge　bulking　to　construct　the　initial　condition　of　KD-FDM　model.　Then,　the　KD-FDM　model　can　obtain　the　precision　parameters　and　compact　structure　in　the　initialization　phase.　Third,　the　KD-FDM　model　using　a　knowledge-aided　fuzzy　broad　learning　system　(KFBLS)　is　employed　to　determine　suppression　strategies　for　sludge　bulking.　Then,　the　KD-FDM　model　can　obtain　fast　and　accurate　strategies　to　mitigate　the　detrimental　impact　on　the　process　performance.　Finally,　the　KD-FDM　model　is　tested　in　a　real　WWTP　to　confirm　its　effectiveness.　The　experimental　results　demonstrate　that　the　proposed　model　can　achieve　outstanding　performance.　IEEE