## 6 Conclusions and Discussion  
Minor agricultural products, characterized by their strong seasonality, regional specificity, and heterogeneous user preferences, present significant challenges for recommendation systems in rural e-commerce platforms: highly sparse interaction data, complex spatio-temporal dependencies, and cold-start scenarios. These challenges have created notable research gaps in developing effective recommendation frameworks specifically tailored to these products. To address these challenges, this study introduces APGCN-CF, a novel framework that synergistically integrates graph convolutional networks with collaborative filtering. Leveraging the unique properties of graph structures, our approach captures high-order connectivity patterns in user-agricultural product interactions while effectively modeling the regional and temporal characteristics inherent to minor agricultural products as well as other key information that influences user purchasing tendencies. Comprehensive experimental evaluations conducted on real-world datasets featuring scallions and garlic demonstrate that our proposed approach consistently outperforms existing state-of-the-art baseline methods across multiple performance metrics, including precision, recall, and F1-score. The significant improvements observed in the ablation studies further validate the effectiveness of the graph convolutional architecture in enhancing node representations and addressing data sparsity challenges, thus providing a robust solution for optimizing minor agricultural product recommendations in rural e-commerce environments. Despite these promising results, several limitations warrant further investigation and improvement in future work. First, the inherent computational complexity of graph convolutional networks may introduce significant performance bottlenecks as data scales increase, with high computational costs potentially limiting the model's practical application in production environments when dealing with large-scale data. In future research, we will investigate graph sampling techniques and distributed computing frameworks to reduce computational costs by decreasing the number of nodes and edges that need to be processed, thereby improving computational efficiency while maintaining recommendation quality. Second, although our framework cleverly incorporates temporal information through graph structures, the seasonal patterns of small-scale agricultural products are influenced by multiple factors, including climate change, cultural festivals, and agricultural cycles, and existing temporal encodings may not fully capture these subtle temporal dependencies. We plan to develop an adaptive temporal encoding mechanism that automatically adjusts according to the specific seasonal patterns of different agricultural products and integrates meteorological data and agricultural production cycle information to more precisely capture complex temporal dependencies. The development of privacy-preserving recommendation techniques that protect user data while maintaining recommendation quality will also be prioritized, addressing the growing concerns regarding data privacy in rural e-commerce scenarios while ensuring the practical applicability of advanced recommendation systems in agricultural commerce platforms. **Author Contributions:** Conceptualization, K.Z. ; methodology, K.Z. ; software, W.H. ; validation, W.H. ; formal analysis, K.Z. and W.H. ; investigation, K.Z. ; resources, K.Z. and P.L. ; data curation, K.Z. and P.L. ; writing--original draft preparation, W.H., K.Z. and X.Y. ; writing--review and editing, W.H., K.Z. and X.Y. ; visualization, W.H. and Y.Z. ; supervision, K.Z. ; project administration, K.Z. and H.J. ; funding acquisition, K.Z. All authors have read and agreed to the published version of the manuscript.