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This paper is to introduce a model. In the analysis of contract risk recognition, redundant variables in the samples spoil the performance of the SVM classifier and reduce the recognition accuracy. On the other hand, we usually canpsilat label one risk as absolutely good, or absolutely bad. In order to solve the problems mentioned above, this paper used rough sets (RS) as a preprocessor of SVM to select a subset of input variables and employ fuzzy support vector machine (FSVM), proposed in previous papers, to treat every sample as both positive and negative classes, but with different memberships. Additionally, the proposed RS-FSVM with membership based on affinity is tested on two different datasets. Then we compared the accuracies of proposed RS-FSVM model with other three models. Especially, in application of the proposed method, training sets are selected by increasing proportion. Experimental results showed that the RS-SVM model performed the best recognition accuracy and generalization, implying that the hybrid of RS with fuzzy SVM model can serve as a promising alternative for recognizing contract risk.