With breakthroughs in next‑generation artificial intelligence technologies, deep learning has introduced anovel paradigm for mineral resource prediction. Traditional prospecting methods often encounter challenges when handlinglarge‑scale, heterogeneous geological datasets, such as uneven spatial distribution, complex nonlinear relationships,and low feature extraction efficiency. Taking the Wulonggou area as a case study, this paper proposes an intelligentgold prospecting prediction method based on the CNN2D model, aiming to address the integration of multi‑source dataand the mining of nonlinear features. The method integrates 3 types of heterogeneous data—geological, geophysical,and geochemical—and designs and trains a CNN2D model for gold prospecting prediction. Results show that the modelachieves the best prediction performance when all 3 data types are integrated, with high accuracy. The predicted areaaccounts for 10.13% of the total study area, with the delineated targets P03, P05, and P07 exhibiting favorable metallogenicconditions, making them viable targets for further exploration. Field investigations and comparison with previous researchindicate that the prediction results are consistent with known metallogenic patterns and demonstrate strong prospectingpotential, further validating the method’s effectiveness. This study marks application of the CNN2D model for prospectingin a complex tectonic region on the plateau, offering an interpretable and generalizable intelligent solution for deep mineralprediction.