The steep bedding rock slope (SBRS) is easily destabilized under earthquake action, so it is crucial to research the features of this kind of slope’s seismic dynamic reactions in order to prevent and mitigate disasters. Few researchers have examined these slopes from an energy perspective, and the majority of recent research focuses on the displacement and acceleration response patterns of these kinds of slopes under seismic action. This work performed an extended study of a dynamic numerical simulation and systematically analyzed the dynamic response characteristics of this type of slope under earth quake conditions from the standpoint of energy utilizing the Hilbert–Huang transform (HHT) and marginal spectrum (MSP) theory. This was carried out in response to the slope’s shaking table test from our previous work. The findings indicate the following: (1) The ‘elevation effect’ and ‘surface effect’ are clearly seen in the acceleration amplification factor (AAF) of the slope during an earthquake. The selectivity of the slope acceleration’s Fourier spectrum amplification impact indicates that the elevation amplification effect makes the high-frequency peak’s amplitude more noticeable. (2) Although the effect of the weak layer is more pronounced in the high-frequency portion, both the elevation and the weak layer affect the seismic wave’s Hilbert energy. As a result, the weak layer at the top of the slope is usually destroyed first during an earthquake. (3) Prior to the locked segment’s penetration failure at the toe of the SBRS, the Hilbert energy of the high-frequency band of the marginal spectrum at the monitoring point on the top portion of the segment will rise sharply. This suggests that the upper portion of the locked segment has begun to sustain damage. There are antecedents even when there is no penetration failure.

In this study, landslide risk assessment was conducted in the Renhe District, Panzhihua City, China. Firstly, based on 190 landslide points and 10 influencing factors, the landslide hazard was assessed using three models: random forest (RF), eXtreme Gradient Boosting (XGBoost), and Tabular Prior-data Fitted Network (TabPFN). The results indicate that the RF and XGBoost models exhibit comparable performance, both demonstrating strong generalization and accuracy, with the RF model achieving superior generalization, as evidenced by an area-under-the-curve (AUC) value of 0.9471. While the AUC value of TabPFN is 0.9243, indicating higher accuracy, it also poses a risk of overfitting and is therefore more suitable for applications involving small sample sizes and the need for rapid responses. The vulnerability assessment utilized the Analytic Hierarchy Process (AHP) to determine the weights of four disaster-bearing bodies, with sensitivity analysis revealing that road type was the most sensitive vulnerability factor. Finally, the landslide risk-assessment map of the Renhe District was produced by integrating the landslide hazard assessment map with the vulnerability assessment map. The findings indicate that the high-risk zones comprised 2.08% of the research region, which includes three principal train stations and necessitates enhanced protective measures. The medium-risk zones comprise 34.23% of the total area and are scattered throughout the region. It is important to enhance local capabilities for landslide monitoring and early warning systems. Relevant conclusions can provide a significant reference for landslide disaster prevention and mitigation work in the Renhe District and help ensure the safe operation of public transport infrastructure, such as railway stations and airports in the district.