Establishing a three-dimensional geological model from a borehole is crucial for providing a coherent understanding of complex underground geology, but traditional Kriging interpolation methods are limited by the subjectivity and fixed form of the selection of the variation function model in geological modeling, making it difficult to accurately reflect the fluctuation characteristics of spatial data. Therefore, a nonlinear mapping relationship between experimental semi variogram function and spatial distance was proposed through support vector mechanism, and a Kriging interpolation algorithm was used to improve fitting accuracy by optimizing key parameters of support vector machine using whale optimization algorithm. In a case study of the Niushan west calcite deposit in Anhui Province, a three-dimensional geological model of the calcite mine was constructed to verify the effectiveness of the improved algorithm. In the experiment, the mean square error was selected as the algorithm performance indicator. Compared with the pre optimized Kriging interpolation method, the optimized Kriging method reduced the root mean square error of the verification boreholes in ZK602, ZK1001, and ZK1002 from 2.06 to 1.25, a decrease of 38%. The interpolation results of the calcite ore layer showed errors of 0.10, 0.31, and 0.50 m, respectively. The interpolation results of the improved method are closer to the actual values, providing an efficient and reliable technical solution for 3D geological modeling in complex geological environments.