Due to the change of image feature distribution in target domain, the performance of zero-shot classification for remote sensing scenes degrades. To solve this problem, a zero-shot classification algorithm for remote sensing scenes based on locality preservation is proposed. Firstly, in order to reduce redundant information, the analysis dictionary learning method was exploited to embed the image features and word vectors of the source domain into the common sparse coefficient space, and the sparse coefficients were compulsively aligned for establishing the relationship between the image features and word vectors. Then, the discriminability of sparse coefficients of scene images was enhanced by preserving the local neighborhood relationship among scene images, which is helpful for clustering analysis on the sparse coefficients. Finally, in order to adapt to the change of image feature distribution, the k-means algorithm was utilized to cluster the sparse coefficients of scene images, and the class labels of the initial centers were used as the scene class labels. With the UCM remote sensing scene dataset as the source domain, zero-shot classification experiments were carried out on RSSCN7 scene dataset of the target domain via two type image features, i.e., GoogLeNet and VGGNet. The highest overall accuracies of 50.67% and 53.29% are obtained, which outperform the state-of-the-art algorithms by 8.06% and 9.70%, respectively. The experimental results show that this method can adapt to the feature distribution of remote sensing scenes, and significantly improve the zero-shot classification performance with certain advantages.