In high-dimensional data spaces, there exists a large number of redundant or useless attributes, and therefore it might be more effective to find target class in their subspaces. A weighted MKnn algorithm based on the Category’s Gini Coefficient subspace is proposed in this paper. Using the Category's Gini Coefficient, the algorithm firstly calculates the corresponding soft subspaces, and projects the training and testing samples onto each category subspaces. Secondly, it calculates the vote weights of unclassified samples on each subspace by the weighted MKnn algorithm and then accumulates them. Finally, it obtains the category labels of unclassified samples. The experimental results on some UCI public datasets demonstrate the effectiveness of the proposed method.