Genetic　landform　recognition　is　critical　for　understanding　the　composition　of　the　Earth　surface　and　its　dynamic　processes.　The　graph-driven　approach　is　a　significant　paradigm　in　data-driven　Earth　science,　whereas　never　been　reported　in　landform　recognition.　The　construction　of　a　meaningful　graph　structure　for　simulating　entire　landforms　and　the　developed　landform　recognition　framework　based　on　it　are　two　major　challenges.　In　this　context,　we　first　develop　a　novel　graph-driven　deep　learning　(DL)　method　for　genetic　landform　recognition.　Specifically,　inspired　by　the　positive　negative　terrain　concept　in　Earth　science,　we　introduced　a　terrain　structure-based　directed　graph　model　(DPN)　to　model　overall　landforms　as　graph　data　with　geo-meaning.　We　then　develop　a　graph　DL　technique　flow　based　on　a　graph　attention　network　(GAT)　to　leverage　DPN　to　achieve　graph-driven　landform　recognition.　We　construct　a　multi-scale　landform　genesis　dataset　with　seven　genesis　landforms　and　756　000　samples.　Based　on　this　dataset　and　four　test　regions　in　China,　a　series　of　carefully　designed　experiments　demonstrate　that　the　proposed　method　is　accurate,　transferable,　and　scalable　in　genetic　landform　recognition.　As　a　corollary,　this　reveals　that　the　overall　terrain　structure　is　closely　related　to　the　genesis　of　the　landforms.　The　proposed　graph-driven　method　shows　superior　or　comparable　performance　compared　to　different　image-driven　methods　with　an　overall　accuracy　of　91.67%.　This　is　one　of　the　first　extensions　of　graph-driven　methods　to　landform　recognition　with　pioneer　results.　Besides,　the　strategy　of　using　DPN　to　simulate　overall　landform　outperforms　any　regional　terrain　element-based　strategy　in　terms　of　recognition　performance,　which　confirms　the　importance　of　the　proposed　terrain　structure　modeling　technique.　Our　study　highlights　that　the　conflation　of　physical　geomorphic　models　and　new　AI　techniques　presents　a　highly　promising　avenue　for　geographical　inquiry.　©　1980-2012　IEEE.