The invention discloses a GRU neural network Mongolian-Chinese
machine translation method. Firstly, a
translation language is preprocessed; then, an
Encoder-Decoder model is built and trained for a certain scale of Mongolian and Chinese bilingual; the Mongolian and Chinese bilingual linguistic data is subjected to coding unified
processing; and finally, a translation result is obtained on the basis of an
Encoder-Decoder model, wherein the
Encoder-Decoder model is constructed by a neural network, one neural network is LSTM; the
encoder is responsible for Encoder encoding, bidirectional coding setting, namely, forward encoding and reverse encoding are carried out on a source language; the source statement is converted into two vectors which are coded in different directions and have fixed lengths, the other neural network is a GRU and is in charge of Decoder decoding; the decoding is carried out from a forward direction and a reverse direction, namely, the context information is automatically integrated when decoding is carried out to output the target language, thus the length-fixed vector generated by encoding is converted into the target
sentence. According to the method, the characteristics of the Mongolian and Chinese language are combined, so that the expression capability of a Mongolian and Chinese
machine translation system is smoother and closer to human expression, andthe degrees of semantic loss and translation disorder in the translation process are reduced.