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In order to improve fault diagnosis correctness and efficiency of incomplete information system, an intelligent diagnosis method of fault based on rough set(RS), ant colony optimization(ACO) algorithm and redial basic function (RBF) neural network is proposed in this paper. In this intelligent diagnosis method, the combination and condition supplement algorithm is used to deal with the incomplete data with the maximum completeness. The RS as a new mathematical tool is used to remove redundant information in order to obtain the minimum rule set. Then the ACO algorithm is directly used to optimize the weights of RBF neural network in order to establish an optimized RBF neural network model, then the minimum rule set is inputted the optimized RBF neural network model in order to obtain an intelligent diagnosis model. The actual data are used to verify the effectiveness of intelligent diagnosis model. The experiment results show that the proposed intelligent diagnosis method can effectively diagnose the faults of system.