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Credit Risk Identification in small and medium enterprises(SMEs) is a real problem which is necessary to be solved in financial sector. Focusing on 32 small and medium enterprises which had bank loan, dimension of six indicators used to judge whether enterprises had credit risk was reduced to simplify model by adopting the factor analysis method. Then small sample data was trained and simulated in examples to get the model that could identify whether there was credit risk in enterprises by adopting support vector machine(SVM) method. At last, the comparison between SVM method and BP neural network method indicated that SVM method had higher reliability in modeling, and this method was used in credit risk identification in SMEs to identify quickly Whether there was credit risk in enterprise, what is more, to lower loan default rate. Meanwhile, it could help SMEs to identify risk quickly, to improve the ability of risk management and to solve the problem of credit risk identification in SMEs creatively.