Remote　sensing　scene　classification　(RSSC)　is　essential　in　Earth　observation,　with　applications　in　land　use,　environmental　status,　urban　development,　and　disaster　risk　assessment.　However,　redundant　background　interference,　varying　feature　scales,　and　high　interclass　similarity　in　remote　sensing　images　present　significant　challenges　for　RSSC.　To　address　these　challenges,　this　article　proposes　a　novel　hierarchical　graph-enhanced　transformer　network　(HGTNet)　for　RSSC.　Initially,　we　introduce　a　dual　attention　(DA)　module,　which　extracts　key　feature　information　from　both　the　channel　and　spatial　domains,　effectively　suppressing　background　noise.　Subsequently,　we　meticulously　design　a　three-stage　hierarchical　transformer　extractor,　incorporating　a　DA　module　at　the　bottleneck　of　each　stage　to　facilitate　information　exchange　between　different　stages,　in　conjunction　with　the　Swin　transformer　block　to　capture　multiscale　global　visual　information.　Moreover,　we　develop　a　fine-grained　graph　neural　network　extractor　that　constructs　the　spatial　topological　relationships　of　pixel-level　scene　images,　thereby　aiding　in　the　discrimination　of　similar　complex　scene　categories.　Finally,　the　visual　features　and　spatial　structural　features　are　fully　integrated　and　input　into　the　classifier　by　employing　skip　connections.　HGTNet　achieves　classification　accuracies　of　98.47%,　95.75%,　and　96.33%　on　the　aerial　image,　NWPU-RESISC45,　and　OPTIMAL-31　datasets,　respectively,　demonstrating　superior　performance　compared　to　other　state-of-the-art　models.　Extensive　experimental　results　indicate　that　our　proposed　method　effectively　learns　critical　multiscale　visual　features　and　distinguishes　between　similar　complex　scenes,　thereby　significantly　enhancing　the　accuracy　of　RSSC.