In　this　article,　the　dissolved　oxygen　(DO)　concentration　control　problem　in　wastewater　treatment　process　(WWTP)　is　studied.　Unlike　existing　control　strategies　that　control　DO　concentration　at　a　fixed　value,　here　we　develop　a　different　control　framework.　Under　the　proposed　control　framework,　an　intelligent　control　method　of　DO　concentration　based　on　reinforcement　learning　(RL)　algorithm　is　presented　to　resolve　the　DO　concentration　control　problem.　By　using　the　deep　deterministic　policy　gradient　(DDPG)　algorithm,　the　DO　concentration　of　the　fifth　tank　in　the　activated　sludge　reactor　can　be　adjusted　dynamically.　In　addition,　by　designing　two　different　reward　functions　and　by　analysing　the　relationships　among　effluent　quality,　energy　consumption,　and　DO　concentration,　the　target　of　energy-saving　and　emission-reducing　is　achieved.　The　simulation　results　indicate　that　the　designed　control　method　can　reduce　energy　consumption　while　ensuring　that　the　effluent　quality　meet　the　specified　standards.