Application of Convolution Neural Network Algorithm in English Translation

In order to further strengthen the application effect of deep learning neural network algorithm in English translation and solve the problems of unknown words, transitional translation and missing translation that are common in neural network machine translation technology, this paper proposes an English Chinese machine translation model based on convolution neural network semantic segmentation algorithm on the basis of traditional deep learning machine translation technology. The generalized corpus is used to train the model and predict the translation. At the same time, the unlisted words are translated with the help of dictionaries and translation rules. Model experiments show that based on RNN and transformer models, Bleu values are increased by 4.72% and 4.21% respectively, and the translation accuracy of unlisted words on the latter model is increased by 35.16%, which verifies the application effect of convolution neural network algorithm in Chinese-English machine translation.