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With the rapid development of Internet technology and the development of economic globalization, international exchanges in various fields have become increasingly active, and the need for communication between languages has become increasingly clear. As an effective tool, automatic translation can perform equivalent translation between different languages while preserving the original semantics. This is very important in practice. This paper focuses on the Chinese-English machine translation model based on deep neural networks. In this paper, we use the end-to-end encoder and decoder framework to create a neural machine translation model, the machine automatically learns its function, and the data is converted into word vectors in a distributed method and can be directly through the neural network perform the mapping between the source language and the target language. Research experiments show that, by adding part of the voice information to verify the effectiveness of the model performance improvement, the performance of the translation model can be improved. With the superimposition of the number of network layers from two to four, the improvement ratios of each model are 5.90%, 6.1%, 6.0%, and 7.0%, respectively. Among them, the model with an independent recurrent neural network as the network structure has the largest improvement rate and a higher improvement rate, so the system has high availability.

Design of English Automatic Translation System Based on Machine Intelligent Translation and Secure Internet of Things