MKnn is an improved version of the k-nearest neighbor method, but it uses general approach to deal with nominal data, that is, if its value is the same then to 0, different to 1, thus the classification efficiency is suppressed a certain degree on the data sets with more nominal data. The concept of Category's Gini is introduced in this paper to deal with the shortage of the processing on nominal data, which statistics the contribution of samples in same class by its data distribution for its category and takes it as the attribute weight, used to estimate the similarity for different samples. It aims to optimize the MKnn method and promotes its applications. The experimental results demonstrate the effect-tiveness of the proposed method.