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.

区域土地规划的合理性需要通过智能技术进行评估并不断优化。目前，大多数土地规划是根据实际需要临时调整的，因此没有实时动态。为了提高区域土地规划的合理性，本研究基于BP神经网络，结合空间挖掘技术构建区域土地规划模型，并利用BP神经网络和SVM技术建立影响因子值之间的关系模型。和距离比例因子。此外，本研究基于灰色系统理论，运用灰色关联模型对产业结构与土地利用之间的耦合程度进行了测度，并分析了各个因素之间的相关性。此外，经过学习和测试的人工神经网络可用于动态仿真计算。研究结果表明，该算法具有一定的实用效果。