Landform　recognition　is　one　of　the　most　significant　aspects　of　geomorphology　research,　which　is　the　essential　tool　for　landform　classification　and　understanding　geomorphological　processes.　Watershed　object-based　landform　recognition　is　a　new　spot　in　the　field　of　landform　recognition.　However,　in　the　relevant　studies,　the　quantitative　description　of　the　watershed　generally　focused　on　the　overall　terrain　features　of　the　watershed,　which　ignored　the　spatial　structure　and　topological　relationship,　and　internal　mechanism　of　the　watershed.　For　the　first　time,　we　proposed　an　effective　landform　recognition　method　from　the　perspective　of　the　watershed　spatial　structure,　which　is　separated　from　the　previous　studies　that　invariably　used　terrain　indices　or　texture　derivatives.　The　slope　spectrum　method　was　used　herein　to　solve　the　uncertainty　issue　of　the　determination　on　the　watershed　area.　Complex　network　and　P-N　terrain,　which　are　two　effective　methodologies　to　describe　the　spatial　structure　and　topological　relationship　of　the　watershed,　were　adopted　to　simulate　the　spatial　structure　of　the　watershed.　Then,　13　quantitative　indices　were,　respectively,　derived　from　two　kinds　of　watershed　spatial　structures.　With　an　advanced　machine　learning　algorithm　(LightGBM),　experiment　results　showed　that　the　proposed　method　showed　good　comprehensive　performances.　The　overall　accuracy　achieved　91.67%　and　the　Kappa　coefficient　achieved　0.90.　By　comparing　with　the　landform　recognition　using　terrain　indices　or　texture　derivatives,　it　showed　better　performance　and　robustness.　It　was　noted　that,　in　terms　of　loess　ridge　and　loess　hill,　the　proposed　method　can　achieve　higher　accuracy,　which　may　indicate　that　the　proposed　method　is　more　effective　than　the　previous　methods　in　alleviating　the　confusion　of　the　landforms　whose　morphologies　are　complex　and　similar.　In　addition,　the　LightGBM　is　more　suitable　for　the　proposed　method,　since　the　comprehensive　manifestation　of　their　combination　is　better　than　other　machine　learning　methods　by　contrast.　Overall,　the　proposed　method　is　out　of　the　previous　landform　recognition　method　and　provided　new　insights　for　the　field　of　landform　recognition;　experiments　show　the　new　method　is　an　effective　and　valuable　landform　recognition　method　with　great　potential　as　well　as　being　more　suitable　for　watershed　object-based　landform　recognition.