Real-time　and　accurate　measurement　of　NOx　emissions　is　indispensable　to　achieve　closed-loop　control　of　the　denitrification　process　during　municipal　solid　waste　incineration　(MSWI).　To　this　end,　this　paper　proposes　a　NOx　emission　prediction　method　for　the　MSWI　process　based　on　attention　modular　neural　network　(AMNN).　First,　it　simulates　the“divide　and　conquer”characteristics　of　the　brain　network　in　processing　complex　tasks,　and　uses　the　fuzzy　C-means　(FCM)　clustering　algorithm　to　divide　the　task　to　be　predicted　into　multiple　subtasks,　thereby　reducing　the　complexity　of　the　prediction　task.　Second,　to　handle　the　sub-tasks　efficiently,　a　self-organizing　fuzzy　neural　network　(SOFNN)　is　designed　to　construct　the　sub-models,　in　which　a　growing　and　pruning　algorithm　and　an　improved　second-order　learning　algorithm　work　together　to　ensure　both　the　learning　efficiency　and　accuracy.　Then,　the　attention　mechanism　is　utilized　to　integrate　the　sub-models　during　the　testing　or　application　stages,　which　can　further　improve　the　generalization　performance　of　this　AMNN-based　prediction　model.　Finally,　the　proposed　prediction　method　is　verified　by　Mackey-Glass　time　series　and　the　real　data　from　a　MSWI　plant　in　Beijing.　©　2024　Materials　China.　All　rights　reserved.