<p>Superconducting radio-frequency (SRF) cavities are the core components of SRF linear accelerators, making their stable operation considerably important. However, the operational experience from different accelerator laboratories has revealed that SRF faults are the leading cause of short machine downtime trips. When a cavity fault occurs, system experts analyze the time-series data recorded by low-level RF systems and identify the fault type. However, this requires expertise and intuition, posing a major challenge for control-room operators. Here, we propose an expert feature-based machine learning model for automating SRF cavity fault recognition. The main challenge in converting the “expert reasoning" process for SRF faults into a “model inference" process lies in feature extraction, which is attributed to the associated multidimensional and complex time-series waveforms. Existing autoregression-based feature-extraction methods require the signal to be stable and autocorrelated, resulting in difficulty in capturing the abrupt features that exist in several SRF failure patterns. To address these issues, we introduce expertise into the classification model through reasonable feature engineering. We demonstrate the feasibility of this method using the SRF cavity of the China accelerator facility for superheavy elements (CAFE2). Although specific faults in SRF cavities may vary across different accelerators, similarities exist in the RF signals. Therefore, this study provides valuable guidance for fault analysis of the entire SRF community.</p>