Seismic research technologies of power systems focus on the design， analysis and disaster mitigation before earthquakes. To quickly assist the emergency work after earthquakes， this paper proposed a post?earthquake evaluation method facing porcelain cylindrical equipment that uses monitoring data to predict structural stress responses. This method establishes a stress response proxy model by integrating machine learning and swarm intelligence evolution technologies， then builds refined simulation model， and conducts response analyses to form structural response database. Based on this， the proxy model can be trained and evaluated. Once the structural responses can be monitored， the proxy model can supply the stress response rapidly after earthquakes to help the post?disaster detection. A case study was performed using 1100 kV transformer bushing， and the evaluation models were vali? dated by shaking table tests and theoretical model based on distributed parameter system. The results indicate that using accelera? tion monitoring data can accurately evaluate the base stress of porcelain cylindrical equipment. Particle swarm optimization can effi? ciently adjust the internal structures of evaluation models， further increasing the model accuracy. The accuracies of evaluation mod? els were validated by both shaking table tests and theoretical model.