In recent years, band selection has been widely used in hyperspectral image dimensionality reduction processing. However, the commonly used data dimensionality reduction methods have not effectively utilized the information related to the human visual system. The research on target detection of hyperspectral images will undoubtedly have a considerable role in promoting. This paper proposes to apply the theory of visual attention mechanism to the study of band selection and constructs a band selection model of visual attention mechanism for target detection applications. By analyzing and calculating the identifiability of the target and background in the band map and quantifying the discrimination ability of the band to the ground object target and background, a band selection method based on the target visual identifiability is proposed. The LC saliency algorithm is used to analyze the visual saliency targets in the spatial domain, calculate the absolute value of the significance difference between the background and the target, and propose a band selection method based on the structural distribution of LC saliency targets. These two methods are combined with the improved subspace partition method proposed to establish a band selection model of visual attention mechanism for target detection. The model is verified by target detection experiments on hyperspectral remote sensing AVIRIS San Diego public dataset. The results show that the proposed band selection model based on the visual attention mechanism has a good detection effect for target detection applications and realizes data reduction and efficient computing processing.