Deriving meaningful mineralization information from raw geospatial datasets is fundamental to the sustainable evaluation and management of mineral resources. As a cornerstone of mineral resource evaluation, identifying geochemical anomalies often faces the significant challenge of sampling bias in practical applications. Strong spatial unevenness often leads to information loss in traditional geostatistical models, where critical anomaly structures may be over-smoothed or obscured. To address this limitation, this study proposes a knowledge-driven adaptive direct sampling (KD-ADS) framework. This approach functions as a geospatial context-aware reconstruction engine. It integrates a multi-factor knowledge-driven weighting system to prioritize regions with high information value and incorporates a dynamic context-aware neighborhood module that adapts to local statistical characteristics. Using 1268 samples from the Jiulian Mountains tungsten metallogenic belt, ablation studies demonstrate the individual contributions of the knowledge-driven weighting and adaptive neighborhood modules to improving reconstruction accuracy and spatial connectivity. Comparative experiments with the traditional direct sampling (DS) algorithm demonstrate that KD-ADS achieves a more accurate reconstruction of geochemical fields and better preserves discrete high-value mineralization anomalies and spatial heterogeneity under sampling-bias conditions. This approach improves the reproducibility of mineralization enrichment patterns and enhances computational efficiency, providing data science-driven support for sustainable mineral exploration and resource allocation.