Contextualized　neural　language　models　have　gained　much　attention　in　Information　Retrieval　(IR)　with　its　ability　to　achieve　better　text　understanding　by　capturing　contextual　structure.　However,　to　achieve　better　document　understanding,　it　is　necessary　to　involve　global　structure　of　a　document.　In　this　paper,　we　take　the　advantage　of　Graph　Convolutional　Networks　(GCN)　to　model　global　word-relation　structure　of　a　document　to　improve　context-aware　document　ranking.　We　propose　to　build　a　graph　for　a　document　to　model　the　global　structure.　The　nodes　and　edges　of　the　graph　are　constructed　from　contextual　embeddings.　Then　we　apply　graph　convolution　on　the　graph　to　learning　a　new　representation,　and　this　representation　covers　both　contextual　and　global　structure　information.　The　experimental　results　show　that　our　method　outperforms　the　state-of-the-art　contextual　language　models,　which　demonstrate　that　incorporating　global　structure　is　useful　for　improving　document　ranking　and　GCN　is　an　effective　way　to　achieve　it.　©　2020　ACM.