Abstract: Previous distance metric learning algorithms assume that the training data and test data have the same distribution, but the assumption may be not always true in practice. When the training data and test data have different distribution, the distance metric learned from the training data may be not fit for test data. In order to resolve above-mentioned problem, based on NCA(Neighbourhood Components Analysis), this paper propose a novel distance metric learning with probability density ratio estimation , which weight the objective function by applying the probability density ratio. The KNN classification on UCI data sets and Corel images demonstrate that the new method resolve the inconsistent of traditional distance metric learning.