This　paper　describes　the　system　proposed　by　the　BIT-WOW　team　for　NLPCC2022　shared　task　in　Task5　Track1.　The　track　is　about　multi-label　towards　abstracts　of　academic　papers　in　scientific　domain,　which　includes　hierarchical　dependencies　among　1,530　labels.　In　order　to　distinguish　semantic　information　among　hierarchical　label　structures,　we　propose　the　Label-aware　Graph　Convolutional　Network　(LaGCN),　which　uses　Graph　Convolutional　Network　to　capture　the　label　association　through　context-based　label　embedding.　Besides,　curriculum　learning　is　applied　for　domain　adaptation　and　to　mitigate　the　impact　of　a　large　number　of　categories.　The　experiments　show　that:　1)　LaGCN　effectively　models　the　category　information　and　makes　a　considerable　improvement　in　dealing　with　a　large　number　of　categories;　2)　Curriculum　learning　is　beneficial　for　a　single　model　in　the　complex　task.　Our　best　results　were　obtained　by　an　ensemble　model.　According　to　the　official　results,　our　approach　proved　the　best　in　this　track.