Wastewater　treatment　processes　(WWTPs)　have　been　considered　as　complex　control　problems,　because　effluent　water　standard,　stability　and　multioperational　conditions　need　to　be　taken　into　account.　In　this　article,　an　event-driven　model　predictive　control　with　deep　learning　(EMPC-DL)　is　proposed　for　the　control　problems　to　improve　the　running　performance　of　WWTPs.　First,　several　events　are　defined　based　on　different　operational　conditions　reflected　by　operational　data.　Then,　an　event-driven　deep　belief　network　(EDBN)　is　developed　based　on　deep　learning　to　approximate　the　nonlinear　characteristics　of　the　WWTPs.　Second,　a　quadratic　optimization　is　designed　to　solve　the　control　law　of　MPC　based　on　the　predictive　output　of　the　EDBN.　The　major　advantage　of　quadratic　optimization　is　its　efficiency,　which　is　achieved　by　an　efficient　strategy　that　only　needs　one-step　prediction　of　EDBN　during　one-time　rolling　optimization.　Third,　this　article　gives　convergence　and　stability　analysis　of　EMPC-DL.　Finally,　the　feasibility　and　applicability　of　EMPC-DL　are　demonstrated　on　the　benchmark　simulation　model　No.　1　(BSM1).　The　experimental　results　show　that　EMPC-DL　achieves　the　more　satisfactory　performance　in　modeling,　controlling,　and　tracking　water　quality　parameters　than　its　peers.