In recent years, with the development of deep learning, feature extraction methods based on deep learning have shown promising results in hyperspectral data processing. We propose a multi-scale hyperspectral image feature extraction method with an attention mechanism, including two parts that are respectively used to extract spectral features and spatial features. We use a score fusion strategy to combine these features. In the spectral feature extraction network, the attention mechanism is used to alleviate the vanishing gradient problem caused by spectral high-dimension and multi-scale spectral features are extracted. In the spatial feature extraction network, the attention mechanism helps branch networks extract important information by making the network backbone focus on important parts in the neighborhood. Five spectral feature extraction methods, three spatial feature extraction methods and three spatial-spectral joint feature extraction methods are used to perform comparative experiments on three datasets. The experimental results show that the proposed method can steadily and effectively improve the classification accuracy of hyperspectral images.