A comparative study of artificial intelligence policy hot topics between central and local governments in China

In recent years, the rapid advancement of China’s artificial intelligence (AI) sector has prompted extensive policy issuance across central and local governments. Using Shandong Province as a case study, this study employs quantitative text analysis techniques—including Latent Dirichlet Allocation (LDA) topic modeling and Term Frequency-Inverse Document Frequency (TF-IDF)—to examine and contrast AI-related policies between the central and a leading provincial government. Based on 221 policy texts collected from the Peking University (PKU) Law Database (2013–2024), the author identifies 15 key thematic topics for each set of policies. A comparative analysis reveals that, while Shandong’s policies align with national strategic priorities, they place stronger emphasis on leveraging local industrial foundations to deepen AI integration within competitive sectors such as manufacturing, agriculture and marine-related industries. However, Shandong’s policies show relative weaknesses in areas such as safeguard mechanisms and public service applications. These findings provide valuable guidance for local governments seeking to optimize their AI policy frameworks according to regional industrial characteristics while maintaining alignment with national strategic directions.