数字经济是继工业经济、农业经济之后的一种新经济形态、新发展动能、新综合业态，引发了经济社会的深刻变革。与传统经济形式不同，数据是发展数字经济的核心生产要素，并以人工智能、大数据、物联网、云计算、区块链等信息技术作为重要载体，通过与信息技术的融合和创新来改变传统生产方式及促进生产效率的提升。近年来，发展数字经济已形成全球共识，中国数字经济发展势头同样强劲，各地区在数字经济发展上也取得显著成绩，数字经济正成为驱动创新发展的新引擎。因此，厘清数字经济对区域创新效率的影响和溢出效应，对中国创新型国家建设具有重要的理论与实践意义。

       文章依据2011—2020年中国30个省份的面板数据，采用运用基于遗传算法的投影寻踪模型对30个省份数字经济发展水平进行测算，使用三阶段DEA模型对各省份创新效率加以测量，运用空间杜宾模型就数字经济对区域创新效率的影响和溢出效应做了检验分析。研究发现：（1）发展数字经济对提升本区域创新效率影响显著，但溢出效应表现为极化效应大于扩散效应，即本地区数字经济发展会抑制邻近地区创新效率的提升，这一核心结论在经过内生性与稳健性检验后依然成立。（2）各地区数字经济发展水平具有明显的时空异质性，总体发展水平表现为东部地区、中部地区、西部地区递减趋势，且随着时间的推移，发展优势由东部沿海地区演变为东南部沿海地区，并进一步向中部地区扩散的趋势。（3）区域创新效率的莫兰指数为正值，地区间创新效率具有正向的空间自相关性，表现为“高高集聚”和“低低集聚”，但莫兰指数值在中等偏低范围，说明由高向低的空间传导路径尚未发挥显著作用。（4）数字经济对区域创新效率的影响具有显著的地区异质性，除东部地区的回归结果不显著外，中部地区和西部的地区的数字经济对区域创新效率均具有显著的促进作用，但从空间效应看，中部地区表现为极化效应，西部地区则为扩散效应。综合实证研究结果，提出相关政策建议：一是加快推进区域数字经济协调发展。二是加大数字基础设施建设投资力度。三是推动数字经济与实体经济深度融合。四是推动数字创新要素向企业加速集聚。

        The digital economy is a new economic form, new development momentum, and new comprehensive business format following the industrial economy and agricultural economy, which has triggered profound changes in the economy and society. Unlike traditional economic forms, data is the core production factor for the development of the digital economy, and information technologies such as artificial intelligence, big data, the Internet of Things, cloud computing, and blockchain are used as important carriers to change production methods and promote the improvement of production efficiency through integration and innovation with information technology. In recent years, the development of the digital economy has formed a global consensus, and China's digital economy has also maintained a strong momentum. Various regions have also made significant achievements in the development of the digital economy, and the digital economy is becoming a new engine driving innovative development. Therefore, clarifying the impact and spillover effects of the digital economy on regional innovation efficiency has important theoretical and practical significance for the construction of an innovative country in China.

         Based on the panel data of 30 provinces in China from 2011 to 2020, this paper uses the projection pursuit model based on genetic algorithm to measure the development level of digital economy in 30 provinces, uses the three-stage DEA model to measure the innovation efficiency of each province, and uses the spatial Durbin model to test and analyze the impact of digital economy on regional innovation efficiency and spillover effects. The research finds that: (1) The development of digital economy has a significant impact on the improvement of innovation efficiency in the region, but the spillover effect is that the polarization effect is greater than the diffusion effect, that is, the development of digital economy in the region will inhibit the improvement of innovation efficiency in neighboring regions. This core conclusion is still valid after the endogeneity and robustness tests. (2) The development level of digital economy in various regions has obvious spatiotemporal heterogeneity, and the overall development level shows a decreasing trend in the eastern, central, and western regions. Over time, the development advantage evolves from the eastern coastal region to the southeastern coastal region, and further spreads to the central region. (3) The Moran index of regional innovation efficiency is positive, and there is a positive spatial autocorrelation between regions, manifested as "high high agglomeration" and "low low agglomeration". However, the Moran index value is in the range of medium to low, indicating that the spatial transmission path from high to low has not yet played a significant role. (4) The impact of digital economy on regional innovation efficiency has significant regional heterogeneity. Except for the regression results in the eastern region, the digital economy in both the central and western regions has a significant promoting effect on regional innovation efficiency. However, from a spatial perspective, the central region exhibits a polarization effect, while the western region exhibits a diffusion effect. Based on the results of empirical research, relevant policy recommendations are proposed: firstly, accelerate the coordinated development of regional digital economy. The second is to increase investment in digital infrastructure construction. The third is to promote the deep integration of the digital economy and the real economy. The fourth is to accelerate the agglomeration of digital innovation elements towards enterprises.