The evolution of solid-state electrolyte interfaces decisively affects all-solid-state lithium batteries' cycling stability and reliability. However, most studies rely on static analysis or limited aggregated descriptors, failing to capture dynamic interfacial degradation during continuous evolution. To address this, we propose an interfacial stability detection framework integrating LAMMPS-based molecular dynamics (MD) simulations with temporal deep learning, aiming to identify state transitions and provide early instability warnings from atomic trajectories. First, representative solid-state electrolyte interface models are built, and continuous evolution trajectories under varying conditions are generated via MD simulations. Multi-scale temporal features are then extracted to characterize interfacial migration, local structural reconstruction, and anomalous dynamics. Using these sequential representations, a temporal deep learning model detects stability states and anticipates degradation trends. Results show that our method effectively distinguishes stable from unstable interfaces by their evolutionary pathways, outperforming conventional static-descriptor-based methods in detection accuracy, temporal sensitivity, and early-warning capability. Interpretability analysis further reveals stagedependent roles of different features during degradation, offering new insights into key drivers of interface instability. This study provides a temporally aware computational framework for dynamic evaluation, risk diagnosis, and data-driven optimization of solid-state electrolyte interfaces.

solid-state electrolytes, interfacial stability, LAMMPS simulations, temporal deep learning