Based on provincial panel data from 2012 to 2022，this study employs an integrated analytical framework combining Dagum Gini coefficient decomposition，kernel density non - parametric estimation，and Markov chain analysis to systematically investigate the spatial disparities and dynamic evolution of primary healthcare human resource allocation in China. Key findings reveal three principal outcomes. First，the kernel density curve shows a rightward shift over the observation period，with the highest average annual growth rate in the western region（2.65%）and the lowest in the central region（1.86%）. Second，the Dagum Gini coefficient shows a steady declining trend overall. The central region has the lowest Gini coefficient compared to the eastern and western regions. Third，the Markov chain transition probability matrix reveals strong state stability across allocation tiers，indicating high structural stability. The overall level of primary healthcare human resource allocation in China is generally on the rise，but regional differences are still evident. During the continuous optimization of equity，a balanced development pattern in the central region shows a leading role in achieving equilibrium. However， the high stability of the current allocation status requires dynamic breakthroughs through institutional innovation. To address these challenges， we propose three policy recommendations， including strengthening spatial linkages both within and across provinces to reduce geographical disparities， utilizing government - led macro - interventions to optimize equity in resource distribution，improving the primary healthcare service system，and continuously promoting institutional innovation.