Abstract:Deep learning (DL) is driving a paradigm shift in modern spectral analysis, transitioning it from a reliance on expert experience and manual feature engineering towards a data-driven, end-to-end intelligent interpretation framework, owing to its powerful capabilities in automatic feature extraction and complex nonlinear modeling. This paper provides a systematic review of the research progress and application prospects of deep learning in modern spectral analysis. First, we introduce the main deep learning models applicable to spectral data processing, including Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), and Transformers. Subsequently, we focus on the innovative applications of deep learning in key spectral data processing steps, covering spectral denoising, image super-resolution reconstruction, data augmentation, the construction of quantitative and qualitative analysis models, cross-instrument model migration and transfer, as well as multi-source spectral data fusion. Finally, the challenges and future prospects of deep learning in promoting the development of spectral analysis towards precision, real-time operation, and large-scale application are discussed, providing a reference for the technological advancement and practical application in this field.