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中国人工智能发展的时空网络结构及驱动因子研究

Research on the Spatio-temporal Network Structure and Driving Factors of Artificial Intelligence Development in China

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通过直觉模糊层次分析法和动态灰色关联法对中国30个省(区、市)在2011—2021年人工智能发展水平进行评测;通过核密度、趋势面和社会网络分析方法分析中国人工智能发展的时间和空间结构,并探测网络空间分异的驱动因子以了解中国人工智能发展状况.结果显示:中国人工智能发展整体水平呈现明显的逐年增长态势,其中2019、2020和2021年增长速度最快.整体发展指数呈现东部大于西部、南部大于北部的空间格局.空间网络的网络等级度和网络效率逐年增加,但网络连接数和网络密度有逐年减少趋势.中部、西部和东北地区板块类型为主受益板块和双向溢出板块,东部地区板块类型为经纪人板块和净溢出板块.驱动因子中R&D经费投入强度、经济发展水平和信息固定资产投资为中国人工智能发展的主要驱动因子.当R&D经费投入强度和经济发展水平这两个驱动因子与其他驱动因子交互时,对中国人工智能发展水平的倍增效应最为显著.
Using the methods of the Intuitionistic Fuzzy Analytic Hierarchy Process and the Dynamic Grey Re-lational Analysis,this study evaluates the level of artificial intelligence (AI) development in 30 provinces (auton-omous regions,municipalities) in China from 2011 to 2021 . It analyzes the temporal and spatial structure of Chi-na's AI development through analysis on kernel density,trend surface and social network,and explores the driv-ing factors of network spatial differentiation. The results show that:the overall level of China's AI development shows an obvious annual growth trend,with the fastest growth rates in 2019,2020 and 2021;the overall devel-opment index presents a spatial pattern in which the east is higher than the west,and the south is higher than the north;the spatial pattern shows that AI development indices are higher in the eastern and southern regions com-pared to the western and northern ones;network hierarchy and efficiency increase annually,though network con-nectivity and density show a decreasing trend;the central,western,and northeastern regions are classified as main beneficiary blocks and two-way spillover blocks,whereas the eastern regions are categorized as broker blocks and net spillover blocks;key drivers,such as the intensity of R&D expenditure,economic development level,and investment in information fixed assets,are identified as major factors influencing AI development in China;when the two driving factors of R&D expenditure intensity and economic development level interact with other driving factors,the multiplier effect on China's AI development level is the most significant.

artificial intelligence developmenttemporal network structurespatial network structuredriving factorsinteraction effect of driving factors

张丽平、周小亮

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福建江夏学院经济贸易学院,福建福州,350108

福州大学经济与管理学院,福建福州,350108

人工智能发展 时间网络结构 空间网络结构 驱动因子 因子交互作用

2024

福建江夏学院学报
福建江夏学院

福建江夏学院学报

CHSSCD
影响因子:0.179
ISSN:2095-2082
年,卷(期):2024.14(4)