This study comprehensively explores the application of deep learning models in natural language processing (NLP) tasks, and verifies the performance of each model through a series of experiments. The research covers core tasks such as text classification, machine translation and information extraction, and the evaluation indicators include accuracy, F1 score and BLEU score. In addition, this article also explores the application potential of cutting-edge technologies such as small sample learning, reinforcement learning, and cross-modal learning in NLP. The results show that BERT and Transformer models, in particular, exhibit high efficiency and accuracy when processing complex language data. This research not only enhances our understanding of the application of deep learning technology in language tasks, but also provides an experimental basis for further technological innovation and optimization.