Neural machine translation is the most widely used machine translation method at present, and has sound performance in languages with rich corpus resources. However, it does not work well in languages that lack of bilingual data, such as Chinese-Vietnamese. Taking the difference in grammatical structure between different languages into consideration, this study proposes a neural machine translation method that incorporates syntactic parse tree. In this method, a depth-first search is used to obtain the vectorized representation of the syntactic parse tree of the source language, and the translation model is trained by embedding the obtained vectors and the source language embedding as inputs. This method is implemented on Chinese-Vietnamese, language pair and achieves 0.6 BLUE values improvement compared to the baseline system. This experiment shows that the incorporating syntax parse tree can effectively improve the performance of the machine translation model under the resource scarcity.