Existing hyperspectral image classification methods focus on using spatial information without considering the continuity of ground object in spatial distribution. Based on this, this paper proposes a space-spectrum weighted nearest neighbor hyperspectral image classification algorithm. By constructing the neighboring space of the test sample points, the spatial neighboring points in the neighboring space that are inconsistent with the test sample labels are filtered to further remove the interference of heterogeneous points in the neighboring space towards the classification of central pixels and improve salt and pepper effect. According to the spectral similarity between the spatial neighbors and the test pixels, different weights are assigned to the spatial neighboring points, which increases the similarity between similar pixels and the difference between the heterogeneous pixels. The distance between the training sample and the test sample neighboring space is obtained by introducing the regularization coefficient, and the training sample label with minimum distance is selected as the label of the test sample. The overall classification accuracies by this method on the Indian Pines and PaviaU hyperspectral datasets reach 96.75% and 98.54%, respectively, which are higher than those by other algorithms listed in the paper.