The invention discloses a knowledge graph anti-fraud feature extraction method based on BFS and LPA, and the method comprises the steps: 1, carrying out the standardization of original data, converting the original data into labeled data under different dimensions, carrying out the cleaning and conversion, and forming data which conforms to the modeling of a knowledge graph; And step 2, constructing a knowledge graph model, including ontology construction, semantic annotation and information extraction. The method has the advantages that (1) a simple social relation is converted into a knowledge relation, different ontology knowledge is injected into a map, and a knowledge map representation method oriented to the consumer finance field is provided; (2) breadth-first search is introduced to find an entity touch black level, touch black information with different traversal lengths can be extracted after improvement, the feature level is enhanced, and the feature representation modes arediversified; And (3) for a fraudulent group problem in the anti-fraudulent field of consumer finance, entity subgroup information is mined by using an entity subgroup mining method based on label propagation, and a corresponding characteristic variable are extracted to show a relatively good distinguishing characteristic.