Owing　to　the　possible　existence　of　system　failures　and　packet　dropouts　in　the　wastewater　treatment　process　(WWTP),　it　is　difficult　to　obtain　sufficient　data,　which　will　result　in　data　shortage.　Therefore,　it　is　a　challenge　to　design　an　effective　data-driven　controller　with　the　above　data　shortage　issue　for　WWTP.　To　solve　this　problem,　a　syncretic　fuzzy-neural　controller　(SFNC)　was　developed　and　analyzed　in　this　article.　First,　the　knowledge　obtained　from　the　operation　conditions　was　made　full　use　by　a　knowledge　reconstruction　mechanism　to　construct　the　initial　condition　of　SFNC.　Then,　the　proposed　SFNC　was　able　to　obtain　the　accurate　parameters　and　compact　structure　in　the　initialization　phase.　Second,　a　syncretic-form　strategy　(SFS)　was　designed　to　syncretize　the　knowledge　from　the　fuzzy　rules　and　the　data　from　the　operation　process　to　optimize　the　structure　of　SFNC.　Then,　the　adaptability　of　SFNC　can　be　improved　to　achieve　good　control　performance　in　the　presence　of　insufficient　data.　Third,　the　stability　of　the　developed　SFNC　was　proved　by　using　Lyapunov　stability　theorem.　Then,　the　stability　of　SFNC　was　given　to　guarantee　its　successful　application.　Finally,　the　proposed　controller　was　tested　on　the　Benchmark　Simulation　Model　No.1　to　confirm　its　effectiveness.　The　results　demonstrated　that　the　proposed　SFNC　can　achieve　superior　control　performance　than　some　other　existing　controllers.