## 1 Introduction
Their models achieved over 90% accuracy in classification and showed an 8-10% improvement over traditional methods in both classification and regression tasks. Dimensionality reduction confirmed the robustness of the ensemble bagging model as an effective tool for slope stability assessment. [PERSON] et al. [25] improved a slope stability prediction model based on various machine learning techniques to improve the accuracy of Factor of Safety (FS) prediction. This study utilized algorithms, i.e., Gradient Boosting (GB), Support Vector Machine (SVM), Multi-layer Perceptron (MLP), Random Forest (RF), and AdaBoost (AB), using a dataset of 371 slope stability cases and considered factors such as geotechnical and topographical parameters. The results demonstrate that all models performed well, with the optimized GB model achieving the highest accuracy. However, most existing models tend to be relatively simplistic in structure, typically designed to process smaller datasets with less complex features. When applied to more intricate geological data, especially those exhibiting pronounced spatial dependencies and unstructured characteristics, these models often fail to adequately capture the complex interrelationships within the data. They tend to overlook the spatial correlations and multi-layered nonlinear relationships, which are essential for accurately modeling and understanding the underlying geological processes. Graph convolution neural network (GNN) is widely used because it can handle large-scale data. In particular, GNN can use message passing to capture the relationship between nodes without changing the data structure of the original graph, and it can update the status of the nodes with the help of aggregation functions. As GNN can effectively obtain the complex relationship of graph data, in recent years, many scholars have applied GNN to the construction and application of knowledge graph to complete many related tasks, such as knowledge extraction, link prediction, and knowledge reasoning [26]. Several applications show that the combination of knowledge graph and graph neural network can effectively mine various potential associations in domain knowledge graph, thus realizing various prediction and reasoning tasks [27; 28; 29]. As a vital component of artificial intelligence, knowledge graphs play a core role in constructing knowledge systems for geological disasters. Especially in typical geological hazard scenarios such as loess landslides, the influencing factors exhibit characteristics of multi-source, fuzziness, dynamics, and an unstructured nature, making it difficult for traditional analytical methods to comprehensively model their complex relational networks. Therefore, efficiently and accurately extracting geological disaster knowledge from unstructured survey data and organizing it into structured graphs is a key challenge in current research. The introduction of graph neural network (GNN) technology into this task not only facilitates deep semantic modeling and structured representation of geological disaster factors, but it also provides a new approach for graph-based reasoning of complex disaster evolution mechanisms. By constructing a domain-specific knowledge graph centered on loess landslides and integrating GNN's capabilities in relationship modeling and reasoning, machines can gain stronger understanding and associative judgment abilities of geological knowledge. Building upon this foundation, a domain-specific knowledge graph construction method was developed, which systematically organizes geological disaster knowledge, thereby providing a robust basis for intelligent applications such as geological risk assessment and early warning systems. To enhance the practical application of knowledge graph technology, a novel approach is proposed that integrates knowledge graphs with tree-structured graph neural networks for the evaluation of loess slope stability.