Based on previous work, this study proposes that the self-attention mechanism guided by syntactic dependency can integrate syntactic dependency knowledge to improve the performance of Chinese word segmentation so that the self-attention mechanism can only focus on those characters that have syntactic dependency influence on the current character’s word segmentation label and learn their influence degree on the current character. In addition, this study performs positional encoding on the self-attention mechanism guided by syntactic dependency trees. The experimental results show that the model has improved its performance compared with the baseline, and the recognition ability of the model for unregistered words has been strengthened.