<p>The primary task in reducing the risk and hazards of landslides is to quickly identify and implement effective treatment measures. Currently, case-based reasoning (CBR) has shown significant results in landslide treatment decision-support applications, but this method still faces issues such as weak geographic spatial characteristics representation, unreasonable weight assignment, and interference from pseudo-similar cases. In response, this study proposes an auxiliary decision-support model for landslide treatment by integrating knowledge graph and case-based reasoning (KGCBR). First, a landslide treatment knowledge graph (KG) is constructed, and the TransH knowledge embedding model is used to obtain the geographical similarity of landslide attributes, which is then integrated into case representation to enhance its geographical spatial characteristics representation capability. Second, leveraging the grey wolf optimizer (GWO), an adaptive weight optimization assignment method is devised to obtain the optimal weights of landslide attributes, and the k-nearest neighbors (KNN) algorithm is introduced to retrieve k similar historical cases. Finally, the treatment measures of similar historical cases for different values of k are statistically analyzed and filtered to correct the recommended measures and eliminate the randomness of the results. Experimental results show that the model achieves a minimum recommendation error rate of 16.23%, lower than the case-based reasoning methods based on averaging weighting (24.42%) and entropy weighting (24.00%). It also demonstrates high engineering rationality and reliability in analyzing engineering treatment cases. Overall, the model can recommend widely applicable landslide treatment measures to decision-makers, reducing decision-making time costs and uncertainty, and meeting practical application needs.</p>