Abstract: To provide a theoretical basis and technical support for the future development and protection of traditional villages in the region, an analysis was conducted of the spatial distribution characteristics and influence factors of such villages across Jiangsu, Shandong, Henan, and Anhui provinces. The spatial distribution pattern and influencing factors of 825 traditional villages in 4 Provinces were examined using the nearest neighbor index, kernel density estimate, geographic concentration index, Moran index, and geographical detector methods. The results show that: (1) Traditional villages in the study area exhibit an agglomeration distribution pattern, with all traditional villages in the provincial scale showing a clustered distribution mode, and the agglomeration distribution is significant in Anhui Province; (2) The distribution of traditional villages is imbalanced at the city scale with a concentration in Huangshan City and Xuancheng City in Anhui Province, Pingdingshan City and Xinyang City in Henan Province; (3) The nuclear density analysis reveals that traditional villages in the study area present a cluster distribution pattern of "one main village, two villages, and multiple centers" on the whole, with a more obvious "core-edge" distribution, showing a horizontal "V" shape in space; (4) Density factors such as GDP density have the strongest explanatory power on the spatial differentiation of traditional villages, followed by the density of common roads and rivers, and finally the topographic factors. The spatial differentiation pattern of traditional villages is affected by the interaction of 11 factors, and there are two types of nonlinear enhancement and double enhancement, indicating that the spatial differentiation pattern of traditional villages in this region is significantly affected by the comprehensive influence of multiple factors, among which the interaction of GDP density, general road density, and other 10 factors has a greater impact on the spatial pattern of traditional villages.