## 4 Discussion  
The innovation of this study lies in it being the first-ever proposal of a hybrid approach that combines knowledge graphs with dendrogram neural networks for loess slope stability assessment. This method constructs a comprehensive framework specifically tailored for organizing, expressing, and applying geological disaster data, particularly focusing on unstructured data. Traditional geological disaster assessment methods primarily rely on structured data, which, in practice, poses significant challenges as much of the geological data is unstructured. To address this, this study introduces a new framework by integrating knowledge graphs with deep learning methods, effectively extracting valuable information from unstructured data and transforming it into graph structures for more accurate slope stability predictions. However, despite utilizing various intelligent methods in the knowledge graph construction process, such as automated information extraction and relationship inference, to enhance the efficiency and accuracy of data processing, the inherent complexity and diversity of geological disaster data still require considerable human involvement, particularly in the graph construction and relationship identification phases. Nevertheless, this method provides a novel approach and solution for handling and analyzing unstructured geological data. There is still room for improvement, particularly in increasing automation, reducing human intervention, and improving the adaptability and generalization capability of the model across different datasets. In this study, the loess slope stability knowledge graph that was developed was primarily applied to regions with known geological backgrounds, providing a solid foundation for evaluating the performance and accuracy of the method. However, the adaptability of the method presents new challenges when applied to areas with scattered geological features and diverse morphological characteristics. Specifically, for regions with complex and varied landscapes, the flexibility of the knowledge graph is planned to be enhanced by incorporating region-specific geological features and their interrelationships into the model. This will allow the performance of the method to be assessed in a variety of complex environments and for its applicability to be further optimized. Multiple models were compared with the one proposed in our study, including widely used machine learning models, such as XGBoost and Random Forest, for slope stability evaluation, as well as a deep learning model (the CNN architecture). To ensure a compre  
Figure 8: ROC curve and AUC values for loess slope stability classification. hensive and fair evaluation, the performances of all models trained on the constructed knowledge graph data were systematically compared. As shown in Figure 9, the best performance in terms of AUC was exhibited by our model, achieving a value of 0.9454, significantly higher than those of XGBoost and Random Forest, which had AUC values of 0.8984 and 0.8882, respectively. In contrast, the worst performance was shown by the CNN model, with an AUC of 0.8485. These results suggest that, in comparison to other models, the proposed model offers a more accurate assessment of slope stability. Specifically, the topological structure of the knowledge graph allows the model to better capture and analyze the spatial correlations and interrelationships of factors influencing loess slope stability. In slope stability evaluation, the influencing factors extend beyond local conditions to encompass spatial interactions. The model effectively incorporates this spatial information, thereby enhancing the precision of its predictions. Through comparative analysis, it was observed that while XGBoost and Random Forest perform well within the realm of traditional machine learning methods, they exhibit limitations when handling data characterized by complex spatial relationships.