This study introduces APGCN-CF, an innovative graph convolutional network-based framework designed to address the distinctive challenges of minor agricultural product recommendation in rural e-commerce environments. The framework synergistically integrates graph convolutional networks (GCNs) with collaborative filtering (CF), providing an effective solution to critical challenges in minor agricultural product recommendations. APGCN-CF implements a multi-module architecture encompassing graph structure construction, convolution-based feature learning, user similarity-based candidate generation, and preference probability-driven precise recommendation. Comprehensive experimental evaluations conducted on real-world minor agricultural product datasets, featuring scallions and garlic as representative cases, demonstrate that APGCN-CF consistently outperforms existing state-of-the-art baseline methods across multiple performance metrics, including precision, recall, and F1-score. Notably, the framework exhibits superior performance in challenging scenarios characterized by data sparsity and cold-start conditions through its effective feature extraction and relationship modeling capabilities. Through deep integration of user preference features, APGCN-CF enables the dynamic generation of highly personalized recommendation lists, providing robust decision support for optimizing minor agricultural product sales. This research not only advances the theoretical landscape of recommendation systems but also delivers a practical solution tailored for personalized recommendation within rural e-commerce platforms.