Techniques based on deep learning for hyperspectral image classification can effectively extract features and promote the mining and utilization of the rich information. The performance of existing methods is still limited by the insufficient extraction of shadow information and the inefficient use of features. For information extraction in shadow areas, dynamic stochastic resonance can enhance the signal by using noise to improve the ability of information expression. For feature utilization, the attention mechanism is embedded in the convolutional neural network, which can further extract and fuse from the space dimension and channel dimension based on the high-level features extracted, screen out features more critical to the current task target, so as improving the classification performance. The experimental results show that dynamic stochastic resonance can effectively enhance signal, the classification accuracy on real world dataset Hydice is improved from 96.48% to 97.14%, and is improved by 0.408 4% with convolutional attention block added. Further verification by comparison with other methods, the classification accuracy on Hydice, Indian Pines and Pavia University reaches 97.436 1%, 99.219 5% and 99.929 9% respectively, which has obvious advantages. It is proved that the method is effective and has good classification performance, and has broad application prospects in the field of hyperspectral image classification.