The study of patient-ventilator asynchrony (PVA) is of great significance to improve the respiratory condition of critically ill patients. In current clinical applications, PVA is still detected by visually observing the pressure, flow and volume curves, which is very time-consuming. Therefore, we aim to develop a classification model based on the permutation disalignment index (PDI). Results showed that the accuracy of classification using the PDI feature and the random forest algorithm reached 0.964, the Recall score reached 0.953, and the F1 score was 0.962. It indicates that PDI is a promising feature to detect PVA.