[Objective] This paper uses linked data technology to study the evolution of geographical names in China, aiming to more effectively conduct digital humanity research. [Methods] First, we constructed the knowledge base CGNE_Onto for the evolution of Chinese geographical names. Then, we formulated the strong and weak marker words to identify evolution type sentences from the historical data. Third, we utilized the BERT-BiLSTM-CRF model to identify the time and place name entities from the evolution type sentences. Fourth, we used the newly generated entities as classes to build the ontology knowledge base, which was visualized from the perspective of direct and indirect path relationship. Finally, we analyzed the numbers and reasons of different evolution types in each dynasty. [Results] The proposed model intuitively demonstrated the evolution of geographical names, and provided some new directions for the analysis of geographical names data. [Limitations] The experimental data set needs to be expanded to improve the quality of evolution feature words. [Conclusions] The knowledge base for place names clearly shows their historical evolutions, as well as the evolution types in different dynasties.