Abstract: Yanshanian magmatism is well developed and has obvious metallogenic specificity in Qinzhou-Hangzhou Bays of South China. With the development of in-situ zircon analysis technology, a huge number of zircon composition data has been accumulated in recent years. On the basis of collecting data published by previous researchers, the authors determined the ore-forming potential of rock masses by using zircon REE compositions through big data thinking method, and explored effective geochemical indicators for ore prospecting. Python language was used to program arbitrary combination of elements. A total of 4095 binary diagrams and 121485 ternary diagrams were obtained, and diagrams that could effectively distinguish zircon parent rock metallogenic types were automatically screened out. The results show that different types of ore-forming rocks have different degrees of differentiation. Geochemical indices related to Ce and Eu can be well distinguished, which may result from the oxygen fugacity and water content of magma. Additionally, it is observed that some new element association diagrams (i.e., Dy/Lu-Er/Lu, Gd/Dy-Er/Yb) can distinguish ore-forming types of rock bodies effectively, but the underlying geochemical mechanism has not been fully understood. In brief, the results of geochemical data mining in this paper can be used as the prospecting indicators, which can provide scientific basis for the study and prospecting of Yanshanian hydrothermal deposits in South China, and can also be used to actively explore the application of big data technology in mineralogy.