In recent years, deep learning techniques have made significant research progress in the field of hyperspectral image processing. Hyperspectral images are widely used in agriculture, environmental monitoring, geological exploration and other fields due to their richness in material and spectral information about objects. Deep learning models play an important role in hyperspectral image research and are able to learn feature representations from large-scale high-dimensional data, improving the performance of tasks such as classification, segmentation, and detection. In the study, researchers usually improve and optimize for the features of hyperspectral images by constructing deep learning models such as HSI-ConvNext. It is able to capture the correlation between different bands, thus realizing semantic segmentation and target detection for hyperspectral images. Data augmentation can effectively expand the limited hyperspectral dataset, and migration learning is able to migrate the knowledge of models trained in other domains to hyperspectral image tasks. Realistic hyperspectral images can be generated for data enhancement and model training. In conclusion, the study of hyperspectral images based on the deep learning model HSI-ConvNext provides new methods and techniques for the analysis and application of hyperspectral data. In the future, with the continuous development of deep learning technology, we can expect more innovations and breakthroughs in the field of hyperspectral image processing.