针对含有缺失数据的数据集的不确定性度量问题,分别给出了信息熵和知识粒度的公理化定义,并提出了该环境下的粗糙集的两种不确定性度量,证明了精度是一种信息熵,粗糙度是一种知识粒度,并得出了几个较好的性质,进一步克服了已有缺失数据集不确定性度量的部分局限性,实例验证了不确定性度量的有效性,且适合度量含缺失数据的粗糙集的模糊性和精确性。 Aiming at the problem of uncertainty measurement of datasets containing missing data, the axiomatic definitions of information entropy and knowledge granularity are given separately. Two kinds of uncertainty measures of rough set in this environment are proposed, which proves that the accuracy is one Information entropy and roughness are a kind of knowledge granularity, and some good properties are obtained. Some limitations of the uncertainty measurement of the missing data set are further overcome, and the validity of the uncertainty measure is verified by an example , And is suitable for measuring the ambiguity and accuracy of rough sets containing missing data.