Abstract: 【Purpose/significance】From the perspective of global macro geography, this paper quantitatively explores the geographical gravity center transfer law of world technological activities in a long time series.【Method/process】This study selects patent big data of 50 major countries/regions from 1970 to 2020 as the analysis object. On the basis of deconstructing six analysis dimensions that reflect differ⁃ ent aspects of the pattern evolution of world technological activities, this study uses methods and tools such as spatial statistical analysis, gravity model, solution of geodetic problem and geographic information technology. Quantify and analyze the spatial pattern distribution and spatiotemporal migration characteristics of the six analytical dimensions and the integrated world technological activities geographical gravity center, and summarize the main migration laws of the geographical gravity center.【Result/conclusion】The results show that from 1970 to 2020: (1) The geographical gravity center of the world technological activities has been steadily distributed around 40° north lati⁃ tude for a long time, and has shown a slight southward migration trend. (2) The geographical gravity center of the world technological ac⁃ tivities shows a significant eastward migration trend. (3) Western developed countries/regions are still in a dominant position in world technological activities, but the advantage is gradually weakening. (4) The significant progress made by Asian countries/regions in the world technological activities is the main force leading the shift of geographical gravity center.【Innovation/limitation】This study uses the methods and tools of geography systematically to intuitively present the geographical gravity center transfer law of world technological ac⁃ tivities in the past 50 years. In the future, we will try to reveal the evolution law of world technological activities pattern from the perspec⁃ tives of the central country transfer and the network structure evolution, and carry out reasonable prediction.