In recent years, with the rapid development of deep learning, natural language processing has achieved great progress in many aspects. In the field of text generation, classical Chinese poetry, as an important part of Chinese culture, also attached growing attention. However, the existing researches on neural-network-based classical Chinese poetry generation ignore the semantics contained in Chinese words. A sentence in Chinese is a sequence of characters without spaces, and thus it is of great significance to segment the sentence properly for understanding the original text correctly. Therefore, supposing that the model knows how to segment the sentence, the meaning of the sentence will be more accurately understood. In this paper, we propose a novel model, namely WE-Transformer (Word-Enhanced Transformer), to generate classical Chinese poetry from vernacular Chinese in a supervised approach, which incorporates external Chinese word segmentation knowledge. Our model learns word semantics based on character embeddings by bidirectional LSTM and enhances the quality of generated classical poems based on the Transformer with extra word encoders. Compared to the baselines and state-of-the-art models, our experiments on automatic and human evaluations have demonstrated that our method can bring better performance.