The attention mechanism of neural networks can extract key information from data, and the application of this feature in the selection of hyperspectral bands can help fully learn the interdependence and nonlinear relations between bands and extract more important bands. This paper presents a multi-objective optimization method for hyperspectral band selection based on the attention mechanism. First, the attention module and autoencoder are used to construct the network. Then, one-dimensional spectral data is provided as input to the network; two loss functions are used and combined with the multi-objective optimization method for training. Therefore, the attention module embedded in the network learns the nonlinear relationship between different bands and assigns more weight to the bands with a large amount of information and easy classification, thereby realizing band selection. Finally, the support vector machine classifier and mean spectral divergence are used to validate the performance of the band subset. The experimental results show that the band subset extracted using this method from the Botswana and Indian Pines datasets is more accurate and informative than the subsets extracted using other algorithms. Thus, it is demonstrated that this algorithm is more effective in selecting hyperspectral bands.