Aiming at the problem of how to reasonably and accurately evaluate the credit of the borrower based on the unbalanced credit data, a new method for evaluating the personal credit risk was proposed. Firstly, the personal credit risk assessment index system was constructed, and in order to analyze the importance of each characteristic of the data, IV values of the credit characteristics of the data were calculated with IV model. Secondly, combining with fuzzy mathematics theory, a membership function based on the heterogeneous class hyperplane was designed. At last, combining the traditional support vector machine (SVM), a fuzzy support vector machine (FSVM) based on the heterogeneous class hyperplane was constructed. The results show that IV values of four indexes of personal loan credit risk assessment:, namely the available amount ratio, the number of overdue 30 to 59 days, the number of overdue 90 days or more, and the number of overdue 60 to 89 days are all more than 0.3, which are of great importance, indicating that they have a great impact on the credit assessment of the lender. The constructed membership function can provide different weights for different samples, and can reflect the importance of different samples. The constructed FSVM based on the heterogeneous class hyperplane can effectively improve the accuracy of estimating credit risk for personal loans, which shows the feasibility and the effectiveness of the proposed method.