While advances in satellite hyperspectral technology and machine learning have significantly boosted its application in mineral prospectivity modeling, conventional data-driven approaches often fall short by neglecting the essential geological information that controls mineralization processes. To bridge the gap, this study develops a novel methodology that integrates hyperspectral imagery with critical geological determinants-specifically pluton boundaries and fault systems, establishing a 39-channel comprehensive data set featuring hyperspectral geological information, and introduces an enhanced Graph Convolutional Network (GCN) model. Architectural improvements include the incorporation of residual connections and the systematic application of batch normalization across both residual modules and convolutional layers, substantially stabilizing and accelerating the training process. Validation using ZY-1 02D hyperspectral data from the Dahongliutan area demonstrates that our refined GCN model achieves superior accuracy in identifying mineralized granitic pegmatites. Quantitative evaluations confirm substantial performance gains, with accuracy improvements of 7, 22, and 27 percentage points over the baseline GCN, Convolutional Neural Network, and Support Vector Machine models, respectively. This work establishes an effective and automated framework for high-precision prediction of lithium- and beryllium-mineralized granitic pegmatites via hyperspectral remote sensing.