With the continuous exploitation of surface and shallow mineral resources, the global demand for concealed ore deposit exploration is increasing. However, concealed mineral prospectivity modeling (MPM) requires significant efforts, particularly in terms of methods and technologies for three-dimensional modeling. In this study, we propose a lightweight three-dimensional convolutional neural network (3D CNN) for MPM, which adopts the inception structure of GoogleNet and combines the idea of end-to-end learning. We replace the fully connected layer in the conventional CNN with deconvolution, which can greatly reduce the required parameters during the training process and accelerate the convergence. The proposed method overcomes the disadvantage that other shallow machine learning methods cannot extract spatial neighborhood information, while it can extract crosscorrelations among geological factors and generates less parameters by a lightweight network when facing massive data. Additionally, compared with the patch-wise method commonly used in previous studies, we use the pixel-wise method for end-to-end learning, which not only overcomes the drawbacks of random sampling but also considers the influence of each voxel when calculating the loss function. The three-dimensional multi-source geoscientific characteristics obtained from the geophysical inversion and 3D geological models are not discretized in order to promote effective CNN training while facilitating the ore-controlling representation. Comparing the predicted results between the 3D weight of evidence (WofE) and our proposed 3D CNN method for MPM, our proposed method and WofE delineated 100% of the known mineralization in high-favorability areas with voxel numbers of 70% and 95%, respectively. A case study of a structure-controlled hydrothermal gold deposit in the Sanhetun area of Heilongjiang Province demonstrates that the proposed 3D CNN method performs better than WofE in terms of prediction effectiveness and efficiency and effectively reveals the correlation between mineralization and adjacent ore-controlling characteristics. Moreover, the proposed 3D CNN method can simulate non-linear metallogenic processes and mine hidden relationships to reveal complex orecontrolling characteristics. In conclusion, the proposed 3D CNN method can reduce the exploration effort in 3D MPM, thus greatly improving the efficiency of discovering concealed ore deposits.