Journal of Beijing University of Posts and Telecommunications(Social Sciences Edition) ›› 2021, Vol. 23 ›› Issue (5): 19-30.doi: 10.19722/j.cnki.1008-7729.2021.0166

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Textual Quantitative Study Based on 34 National-level Artificial Intelligence Industrial Policies


  1. School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • Received:2021-08-23 Online:2021-10-30 Published:2021-11-11

Abstract: At present, China has passed the first stage of the “three-step” strategy of artificial intelligence. Entering the high-end link of the global value chain is the main task in the next stage. Accelerating the development of artificial intelligence has reached a consensus not only in China but also in other countries. This study adopts the method of quantitative content analysis of policy literature, and takes 34 national policies closely related to the artificial intelligence industry issued from 2012 to 2020 as the analysis basis. Based on the time when the State Council issued the Development Planning for a New Generation of Artificial Intelligence, two stages are divided to explore the use and distribution of policy tools at the national level and the application changes in the artificial intelligence industry chain. The results show that: (1) Since the promulgation of the Development Planning for a New Generation of Artificial Intelligence, the number of government documents and the frequency of using policy tools are higher than those in the previous stage. There are more and more joint documents issued by departments, and the policy theme develops from macro level to subtle level; (2) The use of policy tools in the two stages is less and less from supply type to environment type to demand type. Moreover, the frequency of the use of environment and demand-oriented policy tools decreased compared with the previous stage; (3) The policies of the two stages involve all levels of the artificial intelligence  industry chain, but the focus is shifted from the basic layer to the application layer. Finally, some policy suggestions are put forward for the above problems, hoping to provide thoughts and inspiration for the development of artificial intelligence industry in the next stage.

Key words:  artificial intelligence, content analysis, policy tools, industry chain

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