Urban　land-use　types,　a　fundamental　aspect　of　urban　planning,　land　management,　and　the　effective　utilization　of　spatial　resources,　are　exhibiting　increasing　complexity.　Efficient　and　scientific　identification　of　large-scale　urban　land-use　types　has　become　a　major　challenge　in　urban　research.　To　address　this,　the　present　study　adopts　a　functional　structure-based　perspective　and　integrates　commercial　AOI　data,　POI　data,　nighttime　light　data,　and　population　distribution　data　to　classify　land　use.　Departing　from　existing　data　weighting　algorithms,　this　research　applies　artificial　intelligence　techniques,　utilizing　the　categorical　information　of　AOI　data　as　labels.　Through　supervised　deep　learning,　urban　land-use　types　are　refined　into　nine　major　categories　and　21　subcategories　across　cities　of　different　scales　and　locations.　Compared　to　SVM,　RF,　and　MLP　models,　the　XGBoost　model　achieved　the　highest　accuracy　in　classifying　urban　construction　land　(weighted　avg　F1　score　=　0.87).　Furthermore,　by　comparing　the　AOI　data　with　real-world　test　datasets,　the　accuracy　and　granularity　of　land-use　classification　were　significantly　enhanced.　Finally,　this　AI　model,　combined　with　remote　sensing　imagery　and　transportation　network　data,　was　used　to　generate　a　land-use　map　for　the　target　city,　offering　insights　into　the　generalizability　of　AI　models　in　urban　land-use　classification.