The fitting training of neural machine translation is easy to fall into a local optimal solution on a lowresource corpus such as Uyghur to Chinese, resulting in the translation result of a single model may not be a global optimal solution. In order to solve this problem, the probability distribution predicted by multiple models is effectively integrated through the ensemble strategy, and multiple translation models are taken as a whole. At the same time, the translation models with opposite decoding directions are integrated by the reordering method based on cross entropy, and the candidate translation with the highest comprehensive score is selected as the output. The experiment on CWMT2015 Uighur-Chinese parallel corpus shows that proposed method has 4.82 BLEU values improvement compared with a single transformer model.