The　wastewater　treatment　process　(WWTP)　is　a　complex　process　containing　multiple　biochemical　reactions　with　nonlinear　and　dynamic　characteristics.　Therefore,　it　is　a　challenge　to　achieve　accurate　control　of　the　wastewater　treatment　process.　To　solve　this　problem,　a　multi-variable　control　of　wastewater　treatment　process　based　on　the　self-organized　recurrent　wavelet　neural　network　(SRWNN)　is　proposed.　Firstly,　to　deal　with　the　dynamicity　of　wastewater　treatment　process,　according　to　the　firing　strength　of　the　wavelet　base,　the　self-organizing　mechanism　is　designed　to　dynamically　adjust　the　structure　of　the　recurrent　wavelet　neural　network　controller　to　improve　the　control　performance.　Then,　an　online　learning　algorithm　combined　with　adaptive　learning　rate　is　used　to　learn　the　parameters　of　controller.　In　addition,　the　stability　of　the　controller　is　proved　by　the　Lyapunov　stability　theorem.　Finally,　the　benchmark　simulation　platform　is　used　to　conduct　simulation.　The　experimental　results　show　that　this　control　method　can　effectively　improve　the　integral　of　absolute　error　(IAE)　and　integral　of　squared　error　(ISE)　of　the　wastewater　treatment　process.　©　2024　Science　Press.　All　rights　reserved.