Membrane　fouling　has　become　a　serious　issue　for　the　safe　operation　of　wastewater　treatment　process　(WWTP).　To　deal　with　this　problem,　this　paper　proposes　a　data-driven　decision-making　method　to　reduce　the　incidence　of　membrane　fouling　in　WWTP.　The　main　novelties　of　this　proposed　data-driven　decision-making　method　are　threefold.　First,　a　long-term　prediction　method,　based　on　a　self-organizing　deep　belief　network　(SDBN)　and　the　multi-step　prediction　strategy,　is　developed　to　predict　the　membrane　permeability.　Second,　a　multi-warning　method,　based　on　an　independent　component　analysis-principal　component　analysis　(ICA-PCA)　algorithm,　is　proposed　to　detect　and　warn　membrane　fouling　with　multiple　indicators.　Third,　a　multi-category　diagnosis　method,　based　on　the　kernel　function,　is　designed　to　diagnose　membrane　fouling　for　providing　the　decision　support.　Finally,　an　intelligent　decision-making　system,　consisting　the　above　methods　and　required　sensors,　is　developed　for　some　real　wastewater　treatment　plants.　The　experimental　results　demonstrated　the　efficiency　and　effectiveness　of　the　proposed　data-driven　decision-making　method.