The　membrane　fouling　phenomenon,reflected　with　various　fouling　characterization　in　the　membrane　bioreactor(MBR)process,is　so　complicated　to　distinguish.This　paper　proposes　a　multivariable　identifi-cation　model(MIM)based　on　a　compacted　cascade　neural　network　to　identify　membrane　fouling　accu-rately.Firstly,a　multivariable　model　is　proposed　to　calculate　multiple　indicators　of　membrane　fouling　using　a　cascade　neural　network,which　could　avoid　the　interference　of　the　overlap　inputs.Secondly,an　unsupervised　pretraining　algorithm　was　developed　with　periodic　information　of　membrane　fouling　to　obtain　the　compact　structure　of　MIM.Thirdly,a　hierarchical　learning　algorithm　was　proposed　to　update　the　parameters　of　MIM　for　improving　the　identification　accuracy　online.Finally,the　proposed　model　was　tested　in　real　plants　to　evaluate　its　efficiency　and　effectiveness.Experimental　results　have　verified　the　benefits　of　the　proposed　method.