Abstract: Deep learning is the core technology of the development of contemporary artificial intelligence, and inte-lligent interpretation of natural resource elements based on deep learning has become a hot research topic. In order to realize the engineering application of deep learning technology in natural resource monitoring and supervision, it is urgent to conduct business-oriented sample classification and build an intelligent interpretation sample database serving natural resource management. In this paper, firstly, the current status of intelligent interpretation tasks and sample set classification were analyzed, and the problems in sample dataset classification were pointed out. Then, based on the existing data foundation, natural resource classification system, and business requirements of natural resource management, a business-oriented intelligent interpretation sample classification method for natural resources was proposed. Next, samples were classified according to four interpretation tasks. Finally, taking Guangdong as an example, a sample classification practice was conducted, and the results showed that this method is feasible and can be effectively used for the construction of a comprehensive interpretation sample library for natural resources in Guangdong Province.