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Urban agglomeration (UA) is an advanced spatial economic form formed and developed in the process of rapid industrialization and urbanization, and an important carrier of urbanization and economic development. The economy has developed rapidly in the recent decades of China, and the UAs have also developed rapidly. However, as a large population country, the population distribution and changes of UAs in China has unique characteristics. Using the fifth, sixth and seventh population census data, spatial auto-correlation and spatial econometric models, we analyzed the spatial-temporal evolution characteristics and influencing factors of population agglomeration in China’s UAs. Results revealed that: 1) from 2000 to 2020, the population gradually converged into UAs, and the characteristics of population agglomeration in different development degree of UAs differ. The higher the development degree of UA, the higher the population agglomeration degree. Besides, UAs are the main area with the most significant population agglomeration degree, and the spatial autocorrelation show that the cities with similar degree tend to be concentrated in space. The urban population gathering in UAs has a certain positive spillover effect on population size of neighboring cities. 2) Economic development and social conditions factors are important factors affecting population agglomeration degree in UAs. The main factors of population gather into UAs are similar with the outside UAs, but the positive promotion of urbanization rate and proportion of tertiary industry in GDP on population agglomeration of UAs in China are enhancing from 2000 to 2020. Meanwhile, the other factors, such as high-quality public services, good urban living environment conditions, high-quality medical and educational resources, are also important factors to promote urban population gather into UAs. This study provides a basis for formulating the development planning of UAs in China, and enriches the relevant theoretical research of population evolution and influencing factors of UAs.