Exploring the relationship between population quality and regional innovation capacity has significant implications for implementing innovation-driven strategies. This study examines the panel data from 31 Chinese provinces (2010–2020) through generalized quantile regression (GQR) to investigate provincial population quality’s impact on regional innovation capacity. K-means clustering analysis categorizes regions based on aggregate innovation capability scores, revealing heterogeneous effects across regional clusters. The results demonstrate an overall positive correlation between population quality and innovation capacity, with varying effect sizes across quantiles. For Type III regions, the regression coefficients of average education years on innovation indicators show significant positive impacts at low-to-medium quantiles, exhibiting diminishing marginal effects as quantiles ascend. Meanwhile, the coefficient of tertiary enrollment per 100,000 population displays an upward trend with increasing quantiles while maintaining positive significance in third category areas. The findings reveal distinct gradient characteristics in population quality’s innovation effects across regional development tiers. Policy recommendations are proposed emphasizing differentiated strategies aligned with regional innovation capacity levels and population quality dimensions. This research provides empirical evidence for optimizing human capital allocation in regional innovation ecosystems.