## 5 Conclusions  
This paper proposed a geographic data argumentation method that incorporates spatial factors into generative adversarial networks, aiming to address the problems of sparsity and cold start, which lead to the poor performance of the rural ecological civilization recommendation system. For geographic datasets with a small number of samples, geographic data can be augmented by studying the distribution of relevant data. At the same time, the enhanced datasets were applied to solve the problem of rural ecological civilization recommendation, which effectively solved the problem of the recommendation accuracy of the recommendation system not being high enough. Although the method proposed in this paper mainly addressed the problem of rural ecological civilization recommendation, we believe that our method has certain universality and can be widely used in other fields of geographic data argumentation. In the future research work, we plan to improve the S-GAN method by further optimizing the structure of the simple GAN. For example, the advanced GAN variants (such as DCGAN and WGAN) would be applied to geographic data augmentation.  
Conceptualization, M.X.; methodology, M.X. and S.W.; software, M.X., C.S. and A.Z.; validation, M.X., C.S. and A.Z.; formal analysis, M.X.; investigation, C.S.; resources, S.W. and Y.Z.; data curation, A.Z. and S.W.; writing--original draft preparation, M.X.; writing--review and editing, S.W. and Z.Z.; visualization, A.Z.; supervision, Z.Z. and Y.Z.; project administration, Z.Z.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Recommendation System**} & \multicolumn{1}{c}{**Evaluation Metrics**} & \
\begin{tabular}{c} **Rural Knowledge Graph** \\ **(Before)** \\ \end{tabular} &
\begin{tabular}{c} **Rural Knowledge Graph** \\ **(After)** \\ \end{tabular} \\ \hline \multirow{3}{*}{RippleNet} & AUC & 0.5933 & 0.8653 \\  & ACC & 0.5806 & **0.9028** \\  & F1 & 0.6027 & **0.8439** \\ \hline \multirow{3}{*}{KGCN} & AUC & 0.6237 & 0.8779 \\  & ACC & 0.6094 & 0.7625 \\  & F1 & **0.6379** & 0.7895 \\ \hline \multirow{3}{*}{KGNN-LS} & AUC & **0.6521** & **0.9097** \\  & ACC & **0.6250** & 0.7946 \\ \cline{1-1}  & F1 & 0.6333 & 0.8185 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Results of two datasets in different recommender systems (in bold are the data with the best performance under this indicator).  
**Funding:** This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (grant number XDA23100100), the Chinese Scholarship Council (grant number 202008320044), and the National Natural Science Foundation of China (grant number 42050101).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors acknowledge the anonymous reviewers and the editors for their constructive comments that helped to improve the paper significantly.  
**Conflicts of Interest:** The authors declare no conflict of interest.