This paper constructs a GIS database from the aspects of data input, storage and processing. Then, it applies a BP neural network learning algorithm to categorize rural ecotourism resources and evaluate their development potential. Finally, it presents four aspects of the challenges of AI technology in rural ecotourism resource development. The rate of discrimination when rural types are divided into two types is overall higher than that when they are divided into three types, and their correct rates are all more than 86%. The best-developed villages among the rural ecotourism resources in %%A are villages J, C, and E, with total development values of 6.5647, 6.5225, and 6.4919, respectively. Villages A (5.5384), G (5.3031), and I (5.5586) are the next best-developed areas, with total development potentials ranging between 5 and 6. The rest of the 4 villages with total development value <5 are under-optimal development villages. Scientific tourism development and planning are essential for the development of rural tourism resources and environmental protection.