In order to obtain ideal hyperspectral images, a real-time optimization method for hyperspectral images based on denoising technology was designed to address the problems existing in current optimization methods. Firstly, analyze the current research progress of hyperspectral image optimization, identify the shortcomings of current methods, collect hyperspectral images, use adaptive threshold wavelet transform to denoise hyperspectral images, improve the quality of hyperspectral images, and then use Retinex theoretical model to enhance the denoised hyperspectral images, enrich the details of hyperspectral images. Finally, use convolutional neural networks for hyperspectral image classification, the test results show that the peak signal-to-noise ratio and average structural similarity of hyperspectral images optimized by this method are 31.18 and 0.981, which improves the quality of hyperspectral images and makes the classification accuracy of hyperspectral images exceed 92%. The optimization time of hyperspectral images is controlled within 4.5 seconds, which has significant advantages compared to other hyperspectral image optimization methods.