Design and implementation of machine translation model based on recurrent neural network

Aiming at the problems of insufficient long-distance dependent modeling and low semantic alignment accuracy in neural machine translation system, this paper designs and implements a multi-layer encoder-decoder translation model based on recurrent neural network. Based on the full analysis of RNN sequence modeling capability, the attention mechanism is introduced to improve the context aggregation effect, and the network hierarchy and training strategy are optimized to achieve stable expression and effective generation of the model under complex semantic structure. Through empirical comparisons on multiple standard datasets, the experimental results show that the proposed model outperforms traditional methods in terms of BLEU, TER and METEOR, and has stronger translation accuracy and semantic retention.