Considering the limited number of training samples of hyperspectral images and the influence of high spectral dimension on classification accuracy, a novel hyperspectral classification algorithm combined dynamic convolution with triplet attention mechanism (TA) is proposed. First, principal component analysis (PCA) is used to remove spectral redundancy. Then, the processed data are input into the modified residual network. Second, dynamic convolution is introduced in the residual network to extract deep and refined features. The TA model is used to interact with cross-dimensional information to focus on the extremely important hyperspectral spatial-spectral features and reduce the impact of useless information. Finally, the Softmax fully connected layer is used to realize the classification of hyperspectral images. The results demonstrate that the classification effect of the proposed algorithm outperforms six other classification algorithms on three public datasets of Pavia University, Kennedy Space Center, and Salinas. Furthermore, the overall classification accuracy of the proposed algorithm reaches 97.49%, 94.21%, and 98.65% on three datasets, respectively.