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Comparative analysis of artificial intelligence policy themes between central and local governments in China: a case study of Shandong province

artificial-intelligence
data-mining-and-machine-learning
text-mining

Abstract

In recent years, with the rapid development of China’s artificial intelligence (AI) industry, governments at all levels have successively issued a series of relevant policy plans. This paper aims to compare the thematic differences between local and central government AI policies through the mining and analysis of policy texts, providing a reference for local governments to optimize their policy structures. The authors collected and screened 221 AI-related policies issued by the central government and the Shandong provincial government between 2013 and 2024, primarily sourced from the PKU Law Database. Based on a systematic organization of the content and timeline of AI-related policies, this study employed quantitative analysis techniques such as Latent Dirichlet Allocation (LDA) topic modeling and Term Frequency–Inverse Document Frequency (TF-IDF) algorithms to conduct policy text mining and analysis. A total of 15 key hot topics were identified from the AI-related policies issued by the central government and Shandong Province, respectively. An in-depth comparative analysis was then conducted to examine differences in content focus and topic classification between the two sets of policies. The findings indicate that, compared to central policies, Shandong’s policies place greater emphasis on leveraging its existing industrial foundations to promote the deep integration of AI with its competitive industries. However, these policies also reveal shortcomings, such as limited support for safeguard mechanisms and public service-oriented initiatives.
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