Abstract: With the acceleration of globalization,the demand for translation is increasing,and the importance of intelligent translation systems is becoming increasingly prominent.This paper deeply studies the application of natural language processing technology based on deep learning in intelligent translation systems.Firstly,the intelligent translation system based on deep learning mainly relies on the architecture of recurrent neural networks,long short-term memory networks,and convolutional neural networks,and achieves high-quality translation through word vector representation and semantic understanding technology.In terms of system architecture,the encoder-decoder framework combined with attention mechanism significantly improves the quality of translation,while the Transformer-based model has made breakthroughs in handling long-distance dependencies.In practical applications,systems such as Google Neural Machine Translation and CUBBITT have achieved near-human translation performance through innovative data enhancement techniques and multilingual model training methods.However,current intelligent translation systems still face significant challenges in dealing with semantic ambiguity,adapting to linguistic diversity,and cross-cultural understanding.To address these issues,researchers have proposed solutions such as multi-source information fusion,cross-language pre-training,and knowledge enhancement,and have made significant progress in evaluation metrics such as accuracy and fluency.The future development of intelligent translation systems will be towards multi-modal fusion,knowledge-driven and lightweight deployment,while also needing to further improve capabilities in low-resource language translation and model interpretability.

Key words: Deep learning, Natural language processing, Neural network machine translation, Intelligent translation systems, Technical challenges