Landform　recognition　has　become　a　key　part　of　geomorphological　research,　which　has　been　widely　concerned　by　scholars.　The　research　of　geomorphic　units　based　on　catchment　has　become　a　hotspot　in　the　field　of　landform　recognition.　Previous　studies　have　generated　a　series　of　new　questions,　such　as　whether　large-scale　landform　types　can　be　identified　based　on　local　catchment　landform　features,　which　landform　description　methods　are　more　adaptable,　and　what　is　the　knowledge　bottleneck　of　current　landform　recognition　methods　based　on　the　catchment.　So,　in　this　paper,　we　selected　sample　areas　representing　five　major　landform　types　in　China,　including　karst,　loess,　periglacial,　aeolian,　and　fluvial.　Based　on　the　complex　network　theory,　we　took　the　complex　network　indicators　and　the　topographic　metrics　as　the　basic　data　sources.　Three　typical　machine　learning　methods,　i.e.,　LightGBM,　XGBoost,　and　RF,　were　used　to　automatically　identify　the　main　geomorphic　types　in　China.　Results　show　that　both　the　complex　network　structure　and　the　terrain　features　of　the　catchment　have　certain　explanatory　power　and　recognition　effect　on　landforms,　and　the　overall　recognition　accuracy　is　77.5%　and　72.5%,　respectively.　Among　the　five　geomorphologic　types　selected,　LightGBM,　XGBoost,　and　RF　machine　learning　methods　have　the　highest　recognition　accuracy　(up　to　100%)　on　periglacic　geomorphology.　Compared　to　a　single　geomorphic　description　data　source,　the　geomorphic　recognition　effect　that　combines　the　two　data　sources　is　significantly　improved.　The　overall　accuracy　using　two　data　sources　is　5%　and　10%　higher　than　that　using　the　single　complex　network　dataset　and　the　single　topographic　dataset,　respectively.　Moreover,　LightGBM　has　better　adaptability　to　the　combination　of　complex　network　and　terrain　factor　feature　sets,　and　the　overall　accuracy　can　reach　82.5%.　In　general,　this　study　expands　the　application　area　and　scope　of　catchment　landform　recognition　methods,　and　provides　a　new　idea　for　the　research　of　catchment　landform　recognition.　©　2023　Journal　of　Geo-information　Science.　All　rights　reserved.