Abstract: Classical Chinese poetry is a precious cultural heritage. It is significant to use the rich information in classical Chinese poetry to further investigate the language, literature and historical development of Chinese culture. However, the knowledge of classical Chinese poetry is highly fragmented. It not only exists in poetry itself, but also is widely distributed in the materials which are used to explain poetry, such as annotations, translations, appreciations, etc. Our aim is to obtain the potential semantic relationship between words and expressions, and use knowledge graph to link them. By doing this, we could integrate fragmented knowledge in a systematic way, which enables us to achieve better reasoning and analysis of classical Chinese poetry knowledge. In this paper, we propose a method to construct classical Chinese poetry knowledge graph (CCP-KG). About building nodes of CCP-KG, we use the improved Apriori algorithm to generate candidate words, then check if the candidate word appears in the annotations to determine when it can be a node of CCP-KG. About building edges of CCP-KG, the semantic relationship between words is established through the annotations, then we use the artificially constructed classical Chinese poetry hierarchical structure to establish the relationship between abstract semantics. Finally, we obtain CCP-KG, which covers every aspect of classical Chinese poetry and contains multi-layer semantic links between words. Taking Tang poetry as an example, CCP-KG can be used to analysis classical Chinese poems in different dimensions. Compared with character-based data analysis, the use of CCP-KG assists literary research more in-depth from the perspective of semantics. Therefore, CCP-KG is necessary in analyzing classical Chinese poems. In addition, CCP-KG can also be applied to various tasks like reasoning and analysis in classical Chinese poetry. We conduct experiments on the tasks of determining the theme of poetry and analyzing the emotion of poetry respectively, showing the effectiveness and application value of our constructed CCP-KG.