One　of　the　most　important　steps　and　the　main　bottleneck　of　the　activated　sludge　wastewater　treatment　process　(WWTP)　is　the　secondary　clarification,　where　sludge　bulking　is　still　a　widespread　problem.　In　this　paper,　an　intelligent　method,　based　on　a　knowledge-leverage-based　fuzzy　neural　network　(KL-FNN),　is　developed　to　predict　sludge　bulking　online.　This　proposed　KL-FNN　can　make　full　use　of　the　data　and　the　existing　knowledge　from　the　operation　of　WWTP.　Meanwhile,　a　transfer　learning　mechanism　is　applied　to　adjust　the　parameters　of　the　proposed　method　to　improve　the　predicting　accuracy.　Finally,　this　proposed　method　is　applied　to　a　real　wastewater　treatment　plant　for　predicting　the　sludge　bulking　risk,　and　then　for　predicting　the　sludge　bulking.　The　experimental　results　indicate　that　the　proposed　prediction　method　can　be　used　as　a　tool　to　achieve　better　performance　and　adaptability　than　the　existing　methods　in　terms　of　predicting　accuracy　for　sludge　bulking.