The results of neural machine translation models in low-resource scenario is generally not as good as that of models under large-scale training data. For this issue, RNN-based and Transformer-based mainstream neural machine translation models are selected for a study on the effect of neural machine translation models in the Indonesian to Chinese low-resource scenario. Several experiments are carried out and through experimental analysis and case studies, as well as an adaptability analysis given to the two models.