Currently, neural machine translation (NMT) is insufficient in capturing the semantic and structural relationships between clauses in complex sentences, which often results in poor discourse coherence of long and complex sentence translation. To address this problem, the paper proposes a Chinese-English NMT approach by integrating the clause alignment knowledge into NMT. Firstly, a labeling scheme combining manual and automatic annotation is introduced to annotate a large-scale clause aligned Chinese-English parallel corpus that provides rich clause-level Chinese-English bilingual alignment knowledge for model training. Then, a NMT model is designed based on clause alignment learning for enhancing the ability of the model to learn the semantic structure relationships between clauses within complex sentences. Experimental results on WMT17, WMT18 and WMT19 Chinese-English translation tasks demonstrate that proposed method can significantly improve the NMT performance. Evaluation and analysis show that proposed method can effectively improve the discourse coherence of complex sentence in Chinese-English machine translation.