Owing　to　high　complexity　and　time-variant　operation,　as　well　as　increasingly　requirements　for　water　quality,　stability,　and　reliability,　the　wastewater　treatment　process　(WWTP)　is　regarded　as　an　adaptive　control　problem.　In　this　study,　a　data-driven　adaptive　control　with　deep　learning　(DRAC-DL)　is　developed　to　improve　the　operational　performance　of　the　WWTP.　First,　a　feedback　controller　is　designed　to　construct　the　closed-loop　control　scheme.　Second,　an　adaptive　deep　belief　network　(ADBN),　based　on　the　data-driven　self-incremental　learning　strategy,　is　proposed　to　approximate　the　ideal　control　law.　Third,　the　stability　of　the　DRAC-DL　scheme　is　analyzed　in　detail.　The　main　advantage　of　DRAC-DL　lies　in　its　improved　robustness　and　efficiency,　which　benefit　from　the　Lyapunov-based　closed-loop　strategy　and　the　efficient　ADBN　controller.　Finally,　the　feasibility　and　applicability　of　DRAC-DL　are　verified　by　two　parts:　1)　simulation　on　the　nonlinear　system　and　2)　application　to　the　WWTP　on　the　benchmark　simulation　model　No.1.　The　experimental　results　show　the　applicability　and　effectiveness,　among　which　DRAC-DL　reduces　the　output　fluctuation　(variance)　by　no　less　than　82%　and　realizes　the　better　stability　and　robustness.