To solve the problem of translating lines of difference in length into English, this article presents a model of neural network recovery (RNN) English translator-based models of end-to-end encoder-decoder. This method promotes machine autonomous learning of features and transforms corpus data into word vectors by constructing end-to-end. By mapping the source language and target language directly through the recurrent neural network and selecting semantic error to construct objective function during training, the influence of each part in semantic can be well balanced, and the alignment information is fully considered, which provides powerful guidance for deep recurrent neural network training. The results of the neural network test define the standard BLEU score by 1.51–11.86. Our test scores and BLEU scores at all levels show that data in equivalence play an important role in modeling. Summary. the English translation model based on the neural repetitive fusion is efficient and stable.