The groundbreaking advancements in deep learning technology establish a new paradigm for interdisciplinary research in “AI + meteorology”， and severe convective weather prediction emerges as a cutting-edge research focus due to its complex dynamical characteristics and significant socioeconomic impacts. Therefore， the theoretical advancements and methodological innovations of Deep Neural Networks （DNN） in severe convective weather prediction were reviewed systemically， and their specific applications were explored deeply. Firstly， based on the spatio-temporal sequence prediction paradigm， the mechanisms of high-frequency feature extraction by Recurrent Neural Networks （RNNs） and non-RNNs in meteorological sequence modeling were dissected. Secondly， from a generative modeling perspective， the advantages of Generative Adversarial Network （GAN） and diffusion model in probabilistic prediction of extreme weather events were demonstrated. Thirdly， the theoretical breakthroughs of meteorological large-scale models realizing multimodal data fusion and cross-scale feature learning via pre-training and fine-tuning paradigms were revealed， along with their generalization enhancement mechanisms in global numerical weather prediction. Fourthly， aiming at model evaluation systems， the limitations of traditional statistical metrics in extreme weather prediction were analyzed， and pathways for constructing novel evaluation frameworks such as physical consistency constraints were discussed. Finally， the key scientific challenges faced currently and future research directions were distilled， aiming to provide theoretical support and methodological references for constructing the next-generation intelligent system for severe convective weather prediction.