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In the field of natural language processing, recursive neural networks are widely used in machine translation.In addition to other languages, Chinese contains a large number of words, and improving the quality of machine translation from English to Chinese is an important contribution to Chinese processing.This paper introduces the architecture of an English-Chinese machine translation system, which uses knowledge-based context vectors to map English and Chinese words, and is implemented by codec recursive neural network.This paper tests the performance based on the activation function model, and the test results show that the linear activation function of the encoder layer and the hyperbolic tangent activation function of the decoder layer have the best performance.From the execution of GRU and LSTM layer, GRU is better than LSTM.The attention layer is set by using Softmax and Sigmoid activation functions, and the method of this model is superior to existing systems in cross-entropy loss measurement.