In　recent　years,　with　the　rapid　development　of　the　new　energy　industry,　the　probability　of　enterprises　facing　financial　risks　is　also　increasing.　Therefore,　an　effective　early　warning　model　is　needed　to　help　enterprises　timely　detect　crises　and　adjust　their　marketing　strategies.　In　this　paper,　20　listed　companies　in　the　new　energy　industry　were　selected　for　comparison　through　principal　component　analysis　and　logistic　regression　model.　KMO　and　Bartlett　tests　were　used　to　determine　whether　the　selected　indexes　were　suitable　for　principal　component　analysis　and　then　5　principal　components　were　extracted　according　to　the　cumulative　contribution　rate　of　the　factors,　therefore　corresponding　expressions　were　obtained.　In　logistic　regression　analysis,　the　regression　model　was　obtained　through　-2　times　logarithmic　likelihood　function　value　and　Nagekerke　test　to　know　whether　the　model　had　goodness　of　fit.　Finally,　samples　are　selected　to　test　the　model.　The　results　show　that　the　logistic　regression　model　has　a　better　financial　warning　effect　on　new　energy　enterprises.　©　2021　ACM.