为实现语义web上包含复杂模糊知识模糊本体的表示和推理,提出了一种基于vague集的模糊描述逻辑——vague ALC.首先介绍了vague集的定义,然后给出vague ALC的语法和语义的形式化描述,并规定vague ALC知识库中的公理和断言形式,指出了其推理的基本问题,最后给出了vagueALC的检验概念可满足性的tableau推理算法.Vague ALC建立于vague集之上,用一个区间来表示一个成员函数的隶属度,解决了Zadeh模糊集用单一的数不能准确表示一个成员函数隶属度的问题.Vague ALC具有比ALC更强的表达能力,能够表示复杂的模糊知识并基于它们完成推理任务,因此,vague ALC可实现语义web上包含复杂模糊知识的模糊本体的表示和推理. In order to realize the representation and reasoning of the fuzzy ontology containing complex fuzzy knowledge on the semantic web, a vague set vague description logic vague ALC is introduced.First, the definition of vague set is introduced, and then the syntax and semantics of vague ALC Formally describe and define the axioms and forms of assertion in the vague ALC knowledge base, point out the basic problems of its reasoning, and finally give the tableau inference algorithm of the vagueALC test concept satisfiability.Vague ALC is based on vague sets, The membership of a member function is represented by an interval, which solves the problem that Zadeh fuzzy sets can not accurately represent the membership degree of a member function by using a single number.Vague ALC has stronger expression ability than ALC, and can express complicated fuzzy knowledge Therefore, vague ALC can realize the representation and reasoning of fuzzy ontology which contains complex fuzzy knowledge in semantic web.