Research　on　tourism　resource　demand　offers　critical　decision　support　for　the　protection,　development,　and　marketing　of　tourism　resources.　It　also　improves　personalized　experiences　and　satisfaction　for　tourists,　thus　fostering　the　advancement　of　the　tourism　industry　into　broader　and　higher　realms.　It　also　improves　personalized　experiences　and　satisfaction　for　tourists,　thus　fostering　the　advancement　of　the　tourism　industry　into　broader　and　higher　realms.　The　objective　of　this　study　is　to　extract　characteristics　of　3A-level　and　above　scenic　spots　nationwide,　facilitating　the　integration　and　utilization　of　tourism　resources　in　the　country.　The　objective　of　this　study　is　to　extract　characteristics　of　3A-level　and　above　scenic　spots　nationwide,　facilitating　the　integration　and　utilization　of　tourism　information.　This　facilitates　the　automation　and　efficiency　of　tourism　resource　classification　and　provides　suggestions　for　improving　services　at　these　scenic　areas.　In　response　to　the　abundant　resources　of　major　scenic　areas　across　the　country,　a　new　approach　has　been　developed.　In　response　to　the　abundant　resources　of　major　scenic　areas　across　the　country,　this　study　introduces　a　hierarchical　multi-label　classification　model　for　tourism　resources.　tourism　resource　classification　theme　system　issued　by　the　Ministry　of　Culture　and　Tourism,　and　leveraging　FastText　for　pre-training　on　scenic　area　introductory　texts,　this　research　combines　the　advantages　of　the　hierarchical　multi-label　classification　model　with　the　theme　system　of　the　Ministry　of　Culture　and　Tourism.　introductory　texts,　this　research　combines　the　traditional　LSTM　model　with　an　attention-based　Transformer　model.　Additionally,　a　Graph-Convolutional　Network　(GCN)　is　used　to　classify　tourism　resources.　Convolutional　Network　(GCN)　is　employed　as　a　hierarchical　structure-aware　encoder　to　construct　the　MiLCT,　a　hierarchical　multi-label　classification　model,　enabling　sophisticated　multi-label　classification　to　be　applied　to　the　text.　model,　enabling　sophisticated　multi-label　classification　of　tourism　scenic　area　resource　data.　Experimental　comparisons　demonstrate　that,　with　increasing　classification　levels,　the　proposed　model　outperforms　those　lacking　GCN　and　Transformer　components　in　terms　of　micro-Precision,　micro-Recision,　micro-Recision,　and　micro-Recision.　micro-Precision,　micro-Recall,　and　micro-F1　scores,　this　indicates　that　the　hierarchical　structural　information　of　the　model　can　significantly　enhance　its　performance.　In　comparison　to　the　hierarchical　multi-label　correlation　model,　the　proposed　model　demonstrates　enhanced　performance　across　the　evaluated　metrics,　indicating　its　efficacy　in　integrating　multimodal　features　and　providing　more　comprehensive　and　accurate　data　characterization.　©　2024　ACM.