Efficient　and　accurate　decision　is　essential　to　avoid　sludge　bulking　and　ensure　the　stable　operation　of　wastewater　treatment　process　(WWTP).　However,　due　to　the　strong　nonlinear　characteristic,　time-varying　characteristic　and　changeable　triggering　causes　of　sludge　bulking,　the　decision　performance　is　not　optimistic.　In　this　paper,　an　intelligent　decision　method　combining　recursive　kernel　principal　component　analysis　(RKPCA)　and　Bayesian　network　(BN)　is　proposed　to　accurately　detect　the　occurrence,　identify　the　root　causes　and　severity　level,　and　provide　the　restraining　measures　for　sludge　bulking.　The　proposed　intelligent　decision　method　consists　of　three　main　parts.　First,　a　RKPCA　model　based　on　the　sliding　window　mechanism　is　proposed　to　extract　the　nonlinear　and　time-varying　characteristics　of　WWTP.　Then,　the　proposed　RKPCA　can　assist　the　decision　method　to　detect　the　sludge　bulking　with　high　accuracy.　Second,　a　mean　reconstruction　contribution　of　RKPCA　(MRC-RKPCA)　method　is　designed　to　preliminarily　isolate　all　candidate　faulty　relevant　variables　of　sludge　bulking.　Then,　the　MRC-RKPCA　method　is　able　to　provide　the　relationship　between　the　sludge　bulking　and　the　corresponding　faulty　variables　for　the　decision　method.　Third,　a　decision　model　using　BN　is　developed　to　determine　the　root　causes　and　the　severity　of　sludge　bulking　through　reverse　and　forward　inference,　and　provide　corresponding　restraining　measures.　Then,　the　proposed　decision　method　can　obtain　accurate　strategies　to　suppress　the　sludge　bulking.　Finally,　the　proposed　intelligent　decision　method　was　applied　for　a　real　WWTP.　The　results　demonstrate　the　proposed　intelligent　decision　method　can　obtain　better　decision　performance　than　some　existing　methods.