Establishing a 3D orebody model is the foundation of digital mine and smart mine. In response to the phenomenon that the classical radial basis function surface reconstruction algorithm leads to surface boundary self-fitting and model discontinuity when the original data is sparse, this paper proposes a method of implicit automatic modeling of complex orebody with radial basis function incorporating multiple machine learning. This method uses the Stacking model to learn the distribution characteristics of the discrete point cloud data of the orebody contour lines to build a directed point set characterizing the geometric information of the orebody model. On this basis, the boundary points and normal vectors are extracted, the implicit field is established by the Hermite radial basis function, and finally the 3D orebody model is visualized based on the marching tetrahedron algorithm. The analysis was compared with the contour line splicing method, the classical radial basis function surface reconstruction algorithm, and the simple kriging interpolation method. The method can effectively reduce the phenomenon of self-fitting of surface boundaries, reduce redundant holes in the model, and improve the continuity of the model; the similarity between the contour lines cut by the established model and the original contour lines reaches 75.14%, which is comparable to the explicit model with a high degree of manual intervention; the gap between the model and the explicit model in volume characterization reaches the lowest.