Abstract
With the continuous advancement of artificial intelligence in natural language processing technology, machine translation based on machine learning technology has been fully transformed from traditional machine translation methods to neural network machine translation methods. In particular, the tremendous development of large families has made data-driven a reality. With deep learning as the research and design background, the neural network structure of random matrix multivariate statistical analysis is designed according to the language characteristics of English. The model was tested on a Chinese-English panning model, and the optimal model fused with a Bleu value of 39.53. The model results were applied to a real system to achieve language detection, multidirectional language translation, and manual correction of results to be able to learn long dependencies and overcome the limitations of recurrent neural networks to translate long sentences more fluently.
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