The integrative development of industry and tourism is a new trend. Studying the spatial distribution pattern of industrial tourism demonstration sites and causes has important practical significance for promoting industrial tourism to achieve high quality development. Taking 31 provinces and municipalities in China’s mainland as the research object, this study explored the spatial pattern of industrial tourism demonstration sites and influencing factors applying the nearest neighbor analysis, Kernel density estimation, spatial autocorrelation analysis, and hotspot analysis. The results show that: (1) Industrial tourism demonstration site density is greater in the eastern part of China than the west, which is highly consistent with the “Hu Line.” (2) Industrial tourism demonstration sites and urban agglomerations have a high degree of convergence, the spatial distribution pattern of multi-core-grouping based on urban agglomeration is presented with significant spatial agglomeration effects. (3) The neighboring regions of areas with higher concentration of industrial tourism demonstration sites have higher concentration of such sites, showing a certain level of spatial autocorrelation. The correlation degree is east > central > west; the hotspots and cold spots presented a circular pattern from east to west, and there is a typical Matthew effect. (4) There is a significant correlation between industrial development level, transportation conditions, resource endowment, and the spatial distribution of industrial tourism demonstration sites in China. In addition, national policies and local industrial cultural identity are also important factors that led to the spatial differences in industrial tourism demonstration sites. Provinces should leverage on the radiation capabilities of inter-provincial city clusters, metropolitan areas, and urban belts to promote the cross-regional and cross-industry development of industrial tourism, form industrial tourism clusters, and promote the green development of industrial tourism.