A　continuous　stirred-tank　reactor　(CSTR)　system　is　widely　applied　in　wastewater　treatment　processes.　Its　control　is　a　challenging　industrial-process-control　problem　due　to　great　difficulty　to　achieve　accurate　system　identification.　This　work　proposes　a　deep　learning-based　model　predictive　control　(DeepMPC)　to　model　and　control　the　CSTR　system.　The　proposed　DeepMPC　consists　of　a　growing　deep　belief　network　(GDBN)　and　an　optimal　controller.　First,　GDBN　can　automatically　determine　its　size　with　transfer　learning　to　achieve　high　performance　in　system　identification,　and　it　serves　just　as　a　predictive　model　of　a　controlled　system.　The　model　can　accurately　approximate　the　dynamics　of　the　controlled　system　with　a　uniformly　ultimately　bounded　error.　Second,　quadratic　optimization　is　conducted　to　obtain　an　optimal　controller.　This　work　analyzes　the　convergence　and　stability　of　DeepMPC.　Finally,　the　DeepMPC　is　used　to　model　and　control　a　second-order　CSTR　system.　In　the　experiments,　DeepMPC　shows　a　better　performance　in　modeling,　tracking,　and　antidisturbance　than　the　other　state-of-the-art　methods.