Identifying　the　geographic　distribution　and　erosion　risks　of　various　soil　erosion　regions　are　critical　inputs　to　the　implementation　of　extensive　and　effective　land　protection　planning.　To　obtain　more　accurate　and　sufficient　erosion　information　on　a　large　scope,　this　paper　introduced　the　complex　network　theory　to　quantitatively　simulate　the　topographic　spatial　structure　and　topological　relationship　of　the　erosion　area.　The　watershed　was　selected　as　the　basic　study　unit　and　the　directed　weighted　complex　network　(DWCN)　of　each　watershed　was　constructed　from　DEM　data.　The　directed　weighted　complex　network　factor　(DWCNF)　of　each　watershed　was　calculated　by　the　DWCN.　After　combining　DWCNFs　with　existing　SEEF,　the　soil　erosion　types　and　risks　of　sample　areas　in　the　Chinese　Loess　Plateau　were　identified　by　the　random　forest　model.　The　results　show　that　in　both　typical　and　atypical　sample　areas,　the　identification　performance　of　soil　erosion　by　combining　DWCNFs　with　existing　SEEFs　was　performed　better　than　that　by　employing　only　the　DWCNFs　or　SEEFs　dataset.　It　is　suggested　that　the　quantitative　description　of　the　spatial　structure　and　topological　relationship　of　the　watershed　from　the　perspective　of　a　complex　network　contributes　to　obtaining　more　accurate　soil　erosion　information.　The　DWCNF　of　structural　entropy,　betweenness　centrality,　and　degree　centrality　were　of　high　importance,　which　can　reliably　and　effectively　identify　the　types　and　risks　of　soil　erosion,　thus　providing　a　broader　factor　reference　for　relevant　research.　The　method　proposed　in　this　paper　of　vectoring　terrain　into　complex　network　structures　is　also　a　novel　sight　for　geological　research　under　complex　terrain　conditions.