With　the　growing　demand　for　text　analytics,　long　document　classification　(LDC)　has　received　extensive　attention,　and　great　progress　has　been　made.　To　reveal　the　complex　structure　and　extract　the　intrinsic　feature,　the　current　approaches　focus　on　modeling　a　long　sequence　with　sparse　attention　or　representing　word-sentence　or　word-section　relations　partially.　However,　the　thorough　hierarchical　structure　from　words,　sentences　to　sections　of　long　documents　remains　relatively　unexplored.　For　this　purpose,　we　propose　a　novel　Hierarchical　Multi-granularity　Interaction　Graph　Convolutional　Network　(HMIGCN)　for　long　document　classification,　in　which　three　different　granularity　graphs,　i.e.,　section　graph,　sentence　graph　and　word　graph,　are　constructed　hierarchically.　The　section　graph　encapsulates　the　macrostructure　of　a　long　document,　while　the　sentence　and　word　graphs　delve　into　the　document＇s　microstructure.　Notably,　within　the　sentence　graph,　we　introduce　a　Global-Local　Graph　Convolutional　(GLGC)　block　to　adaptively　capture　both　global　and　local　dependency　structures　among　sentence　nodes.　Additionally,　to　integrate　the　three　graph　networks　as　a　whole,　two　well-designed　techniques,　namely　section-guided　pooling　block　and　transfer　fusion　block,　are　proposed　to　train　the　model　jointly　by　promoting　each　other.　Extensive　experiments　on　five　long　document　datasets　show　that　our　model　outperforms　the　existing　state-of-the-art　LDC　models.