The problem of distance structure difference between the word vectors and visual prototypes of remote-sensing scene classification seriously influences the performance of the zero-shot scene classification. Herein, a fusion method based on analytical dictionary learning is proposed to exploit the consistency among the different kinds of word vectors for the performance improvement of the zero-shot scene classification. Firstly, the common sparse coefficients of different kinds of word vectors of scene classification are extracted by analytical dictionary learning method and acted as the fused word vector. Secondly, the visual prototypes are embedded into and structure-aligned with the fused word vector by analytical dictionary learning method similarly, to reduce the distance structure inconsistency. Finally, the prototypes of the unseen classes in the image feature space are obtained via joint optimization, and the nearest neighbor classifier is used to complete the classification of remote-sensing scenes from the unseen classes. Quantitative and qualitative experiments are also conducted on three remote-sensing scene datasets with the fusion of various word vectors. The experimental results show that the fused word vector is more structure-consistent with the prototypes in the image feature space, and the zero-shot classification accuracies of the remote-sensing scenes can be significantly improved.