Hyperspectral Imagery (HSI), based on its high-resolution spatial and spectral information, has important applications in military, aerospace, civil, and other remote sensing fields, which has great research significance. Deep learning has the advantages of strong learning ability, wide coverage, and strong portability, which has become a hot spot in the research of high-precision hyperspectral image classification. Convolutional Neural Networks (CNN) are widely used in the research of hyperspectral image classification because of their powerful feature extraction ability and have achieved effective research results. Still, such methods are usually based on 2D-CNN or 3D-CNN alone. For the single feature of hyperspectral image, the complete feature information of hyperspectral data cannot be fully utilized. Secondly, the local feature optimization of the corresponding extraction network is good, but the overall generalization ability is insufficient. There are limitations in the deep mining of spatial and spectral information of HSI. Because of this, this paper proposes a Hybrid Spectral Convolutional Neural Network Attention Mechanism (HybridSN_AM) based on attention mechanism. The principal component analysis method is used to reduce the dimension of hyperspectral images, and the convolutional neural network is used as the main body of the classification model to screen out more distinguishable features through the attention mechanism so that the model can extract more accurate and more core joint space-spectral information, and realize high-precision classification of hyperspectral images. The proposed method was applied to three datasets: Indian Pines(IP), the University of Pavia (UP), and Salinas (SA). The experimental results show that the overall classification accuracy, average classification accuracy, and kappa coefficient of target images based on this model are higher than 98.14%, 97.17%，and 97.87%. Compared with the conventional HybridSN model, the classification accuracy of the HybridSN_AM model on the three data sets increased by 0.89%, 0.07%，and 0.73%, respectively. It effectively solves the problem of hyperspectral image joint space-spectral feature extraction and fusion, improves the accuracy of HSI classification, and has strong generalization ability. It fully verifies the effectiveness and feasibility of the attention mechanism combined with a hybrid convolutional neural network in hyperspectral image classification, which has important theoretical value for developing and applying hyperspectral image classification technology.