Profiting　from　the　pre-trained　language　representation　models　like　BERT,　the　recently　proposed　document　classification　methods　have　obtained　considerable　improvement.　However,　most　of　these　methods　usually　model　the　document　as　a　sequence　of　text　and　omit　the　structure　information,　which　appears　obviously　in　long　document　composed　of　several　sections　with　assigned　relations.　For　this　purpose,　we　propose　a　novel　Hierarchical　Attention　Transformer　Network　(HATN)　for　long　document　classification,　which　extracts　the　structure　of　the　long　document　by　intra-　and　inter-section　attention　transformers,　and　further　strengths　the　feature　interaction　by　two　fusion　gates:　the　Residual　Fusion　Gate　(RFG)　and　the　Feature　Fusion　Gate　(FFG).　The　proposed　method　is　evaluated　on　three　long　document　datasets　and　the　experimental　results　show　that　our　approach　outperforms　the　related　state-of-the-art　methods.　The　code　will　be　available　at　https://github.com/TengfeiLiu966/HATN