The rationality of regional land planning needs to be evaluated through intelligent technology and continuously optimized. At present, most land planning is temporarily adjusted according to actual needs, so it does not have real-time dynamics. In order to improve the rationality of regional land planning, based on BP neural network, this study combined the spatial mining technology to construct a regional land planning model, and used BP neural network and SVM technology to establish a relationship model between the impact factor value and distance scale factor. Moreover, based on the gray system theory, this study uses the gray correlation model to measure the coupling degree between industrial structure and land use, and analyzes the correlation between various factors. In addition, the artificial neural network after learning and testing can be used for dynamic simulation calculations. The research results show that this algorithm has some practical effects.