Long　document　classification　(LDC)　has　been　a　focused　interest　in　natural　language　processing　(NLP)　recently　with　the　exponential　increase　of　publications.　Based　on　the　pretrained　language　models,　many　LDC　methods　have　been　proposed　and　achieved　considerable　progression.　However,　most　of　the　existing　methods　model　long　documents　as　sequences　of　text　while　omitting　the　document　structure,　thus　limiting　the　capability　of　effectively　representing　long　texts　carrying　structure　information.　To　mitigate　such　limitation,　we　propose　a　novel　hierarchical　graph　convolutional　network　(HGCN)　for　structured　LDC　in　this　article,　in　which　a　section　graph　network　is　proposed　to　model　the　macrostructure　of　a　document　and　a　word　graph　network　with　a　decoupled　graph　convolutional　block　is　designed　to　extract　the　fine-grained　features　of　a　document.　In　addition,　an　interaction　strategy　is　proposed　to　integrate　these　two　networks　as　a　whole　by　propagating　features　between　them.　To　verify　the　effectiveness　of　the　proposed　model,　four　structured　long　document　datasets　are　constructed,　and　the　extensive　experiments　conducted　on　these　datasets　and　another　unstructured　dataset　show　that　the　proposed　method　outperforms　the　state-of-the-art　related　classification　methods.