The human activity pressure index is a comprehensive indicator used to describe the impact of human activities on the terrestrial surface, particularly on terrestrial ecosystems. Conducting quantitative assessments of the pressure exerted by human activities on ecosystems helps in understanding the role of human activities in ecosystem changes. It also provides a basis for targeted human activity regulation and the formulation of ecosystem protection policies. This study constructs a human activity pressure index for terrestrial ecosystems based on pressure intensity and ecosystem sensitivity, using national land use and other data to estimate the index. Additionally, the Monte Carlo simulation method is employed to analyze the uncertainty of the human activity pressure index. The results indicate that in the 2020, the human activity pressure index in China mainly ranged from 0 to 0.2, with higher values in regions such as Tianjin, Shanghai, Shandong, Jiangsu, and Henan, and lower values in Tibet, Qinghai, and Xinjiang. Agriculture was found to contribute the most to the pressure index nationwide, accounting for 38.91%, followed by rural settlements at 21.83%. Notably, in regions such as Yunnan, Guizhou, Chongqing, and Shaanxi, agricultural development's contribution to the human activity pressure index exceeded 60%. Urban development was the major contributor in metropolitan areas like Shanghai, Beijing, Jiangsu, and Tianjin, contributing over 20%. Industrial and mining development played a significant role in regions such as Fujian, Zhejiang, Inner Mongolia, and Guangdong, where their contribution to the pressure index also exceeded 20%. The uncertainty analysis revealed that the proportion of pixels with a human activity pressure index in the low-value range in at least 60% of simulation iterations significantly exceeded those in the high-value range. The similarity between the simulated average values and the original human activity pressure index was significantly lower in high-value areas compared to low-value areas, with a similarity of 0.53 in the 25% low-value range and 0.45 in the 25% high-value range. In seven subregions, the proportion of pixels with at least moderate similarity exceeded 30%, and in three subregions, it exceeded 60%. Therefore, the human activity pressure index is less robust and more uncertain in high-value areas, whereas it is relatively more certain in low-value areas. This study constructs the human activity pressure index by considering pressure sources and ecosystem sensitivity, primarily using remote sensing data as input. This allows for multi-temporal and large-scale assessments of human activity pressure and quantitative evaluations of temporal and spatial changes in human activity pressure. It provides data and methodological support for the analysis and identification of pressure hotspots and ecologically sensitive areas. The findings highlight the varying impacts of different types of land use and development across China's diverse regions, offering valuable insights for policymakers and conservationists.