Identifying　urban　multifractal　structures　are　helpful　for　understanding　urban　spatial　organization　patterns　as　complex　systems.　Multifractal　analysis　is　a　powerful　tool　to　model　multifractal　structures.　However,　due　to　the　use　of　statistical　moments　to　delineate　the　multifractal　spectrum　for　multifractal　analysis,　the　great　majority　of　existing　studies　cannot　map　urban　multifractal　structures　to　geographic　space.　Lack　of　geographic　mapping　makes　it　difficult　to　interpret　the　causes　of　the　anomalous　scaling　characteristics　of　urban　multifractal　structures.　For　the　few　mappable　multifractal　structure　modeling　methods,　they　model　multifractal　structures　from　a　global　or　local　perspective　that　generates　inadequate　or　redundant　scaling　characteristics.　Here,　a　classification-based　multifractal　analysis　method　(CMFA)　was　proposed　to　overcome　the　shortcomings.　It　classifies　the　urban　areas　into　zones　according　to　the　density　of　urban　elements　and　builds　up　the　multi-scaling　relationships　of　urban　elements　for　each　zone.　The　corresponding　multifractal　structures　can　be　mapped　according　to　the　spatial　distribution　of　zones　across　scales.　A　case　study　was　conducted　to　identify　the　multifractal　structures　of　nighttime　light　in　Beijing,　China,　to　verify　the　CMFA　method.　In　conclusion,　when　there　are　abnormal　scaling　characteristics　reflected　by　the　multifractal　spectrum,　the　multifractal　structure　maps　can　diagnose　the　land　use　problems　leading　to　disordered　spatial　organization　patterns.　Urban　planners　should　focus　on　such　problem　land　parcels　and　carry　out　urban　renewal　to　optimize　urban　spatial　structures.　©　2023　Elsevier　Ltd