Due to the small scale of Mongolian-Chinese bilingual parallel corpus and problems such as sparse data and over fitting of data training, the translation quality of traditional statistical machine translation methods for MongolianChinese translation needs to be improved. In view of this situation, we propose a MongolianChinese neural machine translation method based on LSTM. It constructs an end-to-end neural network frame by using the long and short memory model and models the Mongolian-Chinese machine translation system. In order to understand Mongolian sematic information more effectively, Mongolian words are divided into morphemes according to the characteristics of Mongolian language, which are then introduced into the model. Besides, the local attention mechanism is introduced into the model to calculate the weight of the source morphemes that are associated with the target word to achieve the probability of alignment between Mongolian and Chinese vocabularies and improve the translation quality. Experimental results show that compared with the traditional Mongolian-Chinese translation system, the proposed method obtains better translation quality.