Ancient Chinese poetry is the earliest literary genre that took shape in Chinese literature and has a dissemination effect, showing China’s profound cultural heritage. At the same time, the generation of ancient poetry is an important task in the field of digital humanities, which is of great significance to the inheritance of national culture and the education of ancient poetry. The current work in the field of poetry generation is mainly aimed at improving the fluency and structural accuracy of words and sentences, ignoring the theme unity of poetry generation results. In order to solve this problem, this paper proposes a graph neural network poetry theme representation model based on label embedding. On the basis of the network representation of poetry, the topic feature representation of poetry is constructed and learned from the granularity of words. Then, the features of the poetry theme representation model are combined with the autoregressive language model to construct a theme-oriented ancient Chinese poetry generation model TLPG (Poetry Generation with Theme Label). Through machine evaluation and evaluation by experts in related fields, the model proposed in this paper has significantly improved the topic consistency of poetry generation compared with existing work on the premise of ensuring the fluency and format accuracy of poetry.