Cognitive translation is a multi-step, highly complex language processing activity that involves a thorough understanding of the meaning of the original text, precise extraction of key information, precise language conversion, and the production of expressions that meet the cultural background and grammatical requirements of the target language. With the rapid advancement of artificial intelligence technology (AI), deep learning (DL) models have demonstrated outstanding performance in fields such as natural language processing (NLP) and image recognition, achieving a huge technological leap compared to traditional statistical learning methods. In recent years, DL based neural machine translation (NMT) technology has emerged as a dominant technology in the field of machine translation (MT). This article focuses on exploring the application and optimization methods of DL models in cognitive translation, with the goal of improving the accuracy, naturalness, and efficiency of translation. For this purpose, we have designed a DL driven MT model that integrates cutting-edge neural network structures and optimized training strategies. The experimental results show that the model surpasses traditional methods in multiple translation scenarios, significantly improving the quality and efficiency of cognitive translation, and providing a smoother and more efficient tool for cross linguistic communication.