In the context of structural deceleration of economic growth and the transformation of the economy to high quality, it is of great practical significance to explore the effective allocation of R&D human capital for the structural transformation of China's economy. Therefore, based on the provincial panel data from 2009 to 2018 in China, R&D human capital is divided into three types, i.e. R&D human capital with a doctoral degree, master's degree, and bachelor's degree or below, according to the heterogeneity of educational attainment. And then the static and dynamic panel regression models are used to explore the differential impacts of R&D human capital at different education levels on technological innovation from the perspective of total patents and subdivided patents. Statistical analysis and empirical research show that the distribution of R&D human capital at different education levels in China is unbalanced. R&D human capital with a bachelor's degree or below holds the largest proportion, while it has a certain inhibitory effect on regional technological innovation. In contrast, R&D human capital with a master's degree has a significant positive effect on regional technological innovation. In particular, doctoral R&D human capital has no significant positive effect on regional technological innovation, and it has lower innovation output elasticity than master's R&D human capital, and does not show the expected educational advantage in the efficiency of scientific research. The results of this paper have a positive preference for optimizing the allocation of high-level human capital in technological innovation and the transformation of economic structure in China.
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