<p>The effectiveness of geological prospecting depends on the accuracy of prediction of target areas. Compared to traditional qualitative methods, the application of big data concepts and methods to conduct in-depth analyses of the potential value of geological information has proven to be an effective way to improve the accuracy of prospecting target area predictions. The Beishan region of Gansu Province, China, is a prominent polymetallic metallogenic belt in northwest China. In recent years, geologists have encountered challenges in achieving effective breakthroughs in prospecting through conventional methods. In this study, we employ the concepts and methodologies of big data. The research subject encompasses the geochemical and aeromagnetic data from the Beishan region of Gansu Province, where a suite of proprietary software is employed to rectify errors in the initial data set. On this basis, an iron ore prospecting model based on multi-source data information mining has been established. The model has delineated 100 Level I and II preferred target areas with mineral prospecting significance and has constrained these areas to 3.24% of the study area. Following field sampling and verification, the actual mineralization rate of the 38 Level I target areas was determined to be 50%. These results demonstrate the effectiveness of the method proposed in this paper in improving the prediction accuracy of iron ore target areas.</p>