A multi-scale saliency detection-based visual attention mechanism is introduced to eliminate noise and enhance the quality of the hyperspectral images. Further, a hyperspectral image classification method is proposed by combining the clustering dimensionality reduction and visual attention mechanism in accordance with the hierarchical clustering algorithm. Subsequently, dimensionality reduction, acquisition of saliency mapping, and support-vector-machine-supervised classification experiments are conducted by considering the Indian and Pavia datasets as examples. The results denote that the proposed method can considerably improve the classification accuracy and efficiency of hyperspectral images.