In　order　to　overcome　the　shortcomings　of　the　low　accuracy　of　the　mechanism　model　and　poor　physical　interpretability　of　the　data　model,　a　mechanism-data-based　hybrid　model　is　designed　for　the　denitrification　process　of　urban　wastewater　treatment.　To　improve　the　applicability　of　the　model,　a　SASM1　(Simplified　Activated　Sludge　Model　No.1)　is　set　up　by　reasonably　simplifying　the　denitrification　process　of　urban　wastewater　treatment.　To　compensate　the　modeling　errors　of　SASM1,　a　data-driven　model　based　T-S　fuzzy　neural　network　is　established　by　deeply　excavating　the　characteristic　relationships　among　the　wastewater　treatment　data.　A　parallel　structure　that　can　take　both　advantages　of　the　mechanism　model　and　data　model　is　designed.　In　order　to　show　the　effectiveness　of　the　designed　hybrid　model,　some　comparisons　between　the　SASM1　and　former　model　are　presented,　where　four　water　effluent　indicators　are　considered.　The　result　shows　that　the　average　prediction　accuracy　of　effluent　BOD　has　increased　from　79.98%　to　97.82%,　and　the　average　prediction　accuracy　of　COD　has　increased　from　31.92%　to　98.68%,　and　the　average　prediction　accuracy　of　other　water　effluent　indicators　has　also　been　significantly　improved.　©　2022　IEEE.