The position and shape of the slip surfaces in reservoir bank slopes, which is essential for stability analysis and risk evaluation, are dynamically influenced by external factors such as excavation and impoundment. This study proposes a potential multi-slip surfaces automatic identification and rapid extraction method based on energy dissipation rate index (EDR). Firstly, the spatial distribution of the EDR at each moment is calculated via an elasto-viscoplastic model within the internal variable thermodynamic framework. A ridge-finding technique is then applied to the EDR field to build a feature point set for slip surface extraction, which is subsequently denoised through convex hull check and statistical threshold. Density-based spatial clustering of applications with noise (DBSCAN) is adopted for the initial clustering of feature points, followed by multi-model fitting via random sample consensus (RANSAC), enabling the progressive and automatic extraction of primary and secondary slip surfaces even under dense noise conditions. The proposed method is then validated and applied to a reservoir bank slope. The results indicate that the slope has five localized failure modes coexisting with the inherent surface of rupture. The proposed method effectively reduce the need for manual intervention and enhancing the robustness of the program.