The state-of-the-art Chinese word segmentation systems obtain high accuracy in domains like newswire but suffer a significant performance degradation when they are used in other domains such as patents and literature. In this paper, we propose a neural domain adaptation approach which works through cross-domain embeddings and uses unlabeled target domain data to improve the cross-domain performance. Experiment results show that the proposed method achieves competitive performance with previous Chinese word segmentation domain adaptation methods.