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At present, many research teams have conducted in-depth research on minority language machine translation for South and Southeast Asia. However, as the official language of Pakistan, Urdu has limited data resources and a significant gap from Chinese, resulting in a lack of targeted research on Chinese-Urdu machine translation methods. To address this issue, this paper proposes a Chinese-Urdu neural machine translation model based on Transformer and incorporating Urdu part-of-speech sequence prediction. Firstly, Transformer is used to predict the part-of-speech sequence of the target language Urdu. Then, the translation model's prediction results are combined with the part-of-speech sequence prediction model's results to jointly predict the final translation, thereby integrating language knowledge into the translation model. Experimental results on a small-scale Chinese-Urdu dataset show that the proposed method has a BLEU score of 0.13 higher than the baseline model on the dataset, achieving significant improvement. [ABSTRACT FROM AUTHOR]