In　interconnected　industrial　control　networks　like　smart　grids,　topology　attacks　on　physical　grids　can　lead　to　severe　cascading　failures　and　large-scale　blackouts.　Effective　defense　on　vulnerable　devices　can　significantly　reduce　the　risk　of　cascading　failures　and　improve　overall　system　robustness.　In　this　paper,　we　investigate　the　vulnerability　analysis　problem　from　a　graph　theoretical　classification　perspective.　By　calculating　a　node　vulnerability　vector　composed　of　features　based　on　complex　network　theory,　node　embedding,　extended　betweenness　and　power　flow　distribution,　we　propose　a　node　vulnerability　analysis　method　based　on　XGBoost　classifier.　A　cascading　failure　simulation　model　based　on　DC　power　flow　is　used　to　simulate　the　smart　grid　behaviours　under　topology　attacks　and　create　the　dataset　for　the　XGBoost　classifier.　The　effectiveness　of　the　proposed　XGBoost-based　method　with　newly-introduced　features　is　demonstrated　by　case　studies.　©　2021　IEEE.