Rural tourism has important practical significance for optimizing the rural industrial structure, and recovering the rural economy, especially for the implementation of the rural revitalization strategy. Thus, rural tourism is not only the focus of local government and tourism enterprises, but also a hot topic in domestic and international tourism research. At the same time, with the development and popularization of the Internet, travel websites, social software and other online platforms have become important tools to obtain travel information, make travel decisions, and share travel experiences. Tourism big data provides data sources and methodological support for rural tourism research. Based on data of tourism network, this paper puts forward a method for identifying rural tourism hotspots. Taking Jiangsu province as an example, this paper uses the methods of trend surface, nuclear density estimation and hot spot analysis to explore the cold and hot patterns of rural tourism, and reveals the influencing factors of the evolution with the help of geographic detectors. The results show that: (1) The annual and seasonal changes of rural tourism heat are obvious. The annual change presents s an "S" shaped evolution track, seasonal variation is characterized by "three peaks and four valleys", but the degree of seasonal influence on different types of rural tourist attractions is slightly different. (2) The spatial structure of rural tourism in Jiangsu province experienced the evolution of "mononuclear-dual-nuclear-trinuclear" in 2009-2017, but its heterogeneity is still significant, basically maintaining the overall characteristics of "high in the south and east regions, while low in the north and west regions". The hot spots are concentrated in southern Jiangsu and gradually evolve into cold spots in the north. The evolution of spatial structure shows a trend of "expanding from the west to the north". (3) There are obvious strength differences and time variations among the influencing factors. Transportation convenience and reception capacity have always been the main influencing factors. The economic development has a significant positive impact on the early development of rural tourism, and the influence of tourism resource tends to decline. The positive influence of ecological environment and government orientation on rural tourism is increasing. Hotspot identification based on network data provides a new perspective for quantitative research of rural tourism. In terms of practicability, it is helpful to clarify the evolution characteristics of the cold and hot patterns of rural tourism so as to provide important guiding significance for rural tourism resource development and regional cooperation.