Abstract: Based on the theory of biased technological change, this paper explores the influence paths and mechanisms of the multi-factor configuration effects of artificial intelligence technology on the employment structure of the high-tech manufacturing industry. Adopting a mixed research design that combines qualitative and quantitative methods, the authors conducted an empirical analysis using fsQCA on a dataset of 30 provinces from 2018 to 2020. The results show that: (1) There are configuration effects of the antecedent factors of the high-tech manufacturing employment structure change, and none of the factors such as artificial Intelligence-related industry input, industrial robot input, industrial transformation and upgrading, industrial intelligence, working-age population ratio, and regional labor force education level are necessary conditions alone; (2) There are four paths that drive the increase of high-tech manufacturing labor force ratio, including industrial transformation-talent quality type, industrial intelligence-population dividend type, machine substitution-talent scarcity type, and machine intelligence-population dividend type; (3) The combination of manufacturing intelligence level, manufacturing transformation and upgrading degree, and Working-age population ratio, manufacturing intelligence level and labor force education level are the two core conditions that promote the increase of high-tech manufacturing labor force ratio. This study lays a foundation for promoting the transformation of biased technological change from a single perspective to a multidimensional perspective, and provides practical implications for regional management departments to invest in artificial intelligence to achieve healthy development of high-tech manufacturing.