In fuzzy systems, how to represent uncertainty is a crucial research topic. Negation is an inherent characteristic of knowledge, and it provides a brand-new perspective of solving problems from the opposite of the events. Intuitionistic fuzzy sets (IFSs), as a generalization of the fuzzy sets, have the ability to better express fuzzy information. However, since the existing methods have not completely broken through the constraints of the first (classical) negation and inconsistent calculation standards, IFSs still have limitations in expressing uncertainty. To address this issue, and strengthen the performance of fuzzy systems to represent uncertain information, this paper proposed a novel method to obtain the negation of the IFS from the perspective of maximum entropy. Some desired theorems and properties are investigated to denote the nature of the negative IFS. Moreover, entropy is used to describe the connection between the IFS and uncertainty in the negation process. Futhermore, based on the negation, this paper designed a new approach to measure the uncertainty of the IFS. Then, a new pattern classifi- cation algorithm is developed. Finally, the practical applications show the effectiveness of the negation method.