Abstract In this paper, we present a new method for handling classification problems using a new fuzzy information gain measure. Based on the proposed fuzzy information gain measure, we propose an algorithm for constructing membership functions, calculating the class degree of each subset of training instances with respect to each class and calculating the fuzzy entropy of each subset of training instances. Based on the constructed membership function of each fuzzy set of each feature, the obtained class degree of each subset of training instances with respect to each class and the obtained fuzzy entropy of each subset of training instances, we propose an evaluating function for classifying testing instances. The proposed method gets higher average classification accuracy rates than the methods presented in [John, G. H., & Langley, P. (1995). Estimating continuous distributions in Bayesian classifiers. In Proceedings of the 11th conference on uncertainty in artificial intelligence, Montreal, Canada (pp. 338–345); Platt, J. C. (1999). Using analytic QP and sparseness to speed training of support vector machines. In Proceedings of the 13th annual conference on neural information processing systems, Denver, Colorado (pp. 557–563); Quinlan, J. R. (1993). C4.5: Programs for machine learning. San Francisco: Morgan Kaufmann].