A spatial consistency measurement method based on the Weighted Spatial-Spectral Distance(WSSD) is proposed and applied to the K Nearest Neighbor(KNN) classifier, and a new hyperspectral image classification algorithm is obtained. On the basis of the physical characters of hyperspectral images, the proposed algorithm combines both spatial window and spectral factor to obtain the spatial information and spectral information , and uses the spatial nearest points to reconstruct the center point and to reveal the local spatial structure. With effectively reducing the redundant information in the image, this algorithm increases the consistency of the same kinds pixels and the difference of the different kinds pixels and obtains extract discriminating features, so it implements the consistency measurement between the data points. The experiments were performed on the Indian Pines and PaviaU hyperspectral data sets. Experiment results show that the WSSD-KNN algorithm has better classification accuracy than other algorithms when it is applied to the classification of hyperspectral image, and the overall classification accuracies reach 91.72% and 96.56%, respectively. With the spectral information, spatial information and extract discriminating features, the proposed algorithm effectively improves ground object classification accuracy of hyperspectral data and has better recognition ability in less train samples.