In　the　era　of　big　data,　classifying　online　tourism　resource　information　can　facilitate　the　matching　of　user　needs　with　tourism　resources　and　enhance　the　efficiency　of　tourism　resource　integration.　However,　most　research　in　this　field　has　concentrated　on　a　simple　classification　problem　with　a　single　level　of　single　labelling.　In　this　paper,　a　Hierarchical　Label-Aware　Tourism-Informed　Dual　Graph　Attention　Network　(HLT-DGAT)　is　proposed　for　the　complex　multi-level　and　multi-label　classification　presented　by　online　textual　information　about　Chinese　tourism　resources.　This　model　integrates　domain　knowledge　into　a　pre-trained　language　model　and　employs　attention　mechanisms　to　transform　the　text　representation　into　the　label-based　representation.　Subsequently,　the　model　utilizes　dual　Graph　Attention　Network　(GAT),　with　one　component　capturing　vertical　information　and　the　other　capturing　horizontal　information　within　the　label　hierarchy.　The　model＇s　performance　is　validated　on　two　commonly　used　public　datasets　as　well　as　on　a　manually　curated　Chinese　tourism　resource　dataset,　which　consists　of　online　textual　overviews　of　Chinese　tourism　resources　above　3A　level.　Experimental　results　indicate　that　HLT-DGAT　demonstrates　superiority　in　threshold-based　and　area-under-curve　evaluation　metrics.　Specifically,　the　AU(PRC‾)　reaches　64.5　%　on　the　Chinese　tourism　resource　dataset　with　enforced　leaf　nodes,　which　is　3　%　higher　than　the　optimal　corresponding　metric　of　the　baseline　model.　Furthermore,　ablation　studies　show　that　(1)　integrating　domain　knowledge,　(2)　combining　local　information,　(3)　considering　label　dependencies　within　the　same　level　of　label　hierarchy,　and　(4)　merging　dynamic　reconstruction　can　enhance　overall　model　performance.　©　2024　Elsevier　Ltd