Abstract: To improve the identification efficiency of disturbance events in groundwater observation data, decision tree algorithm is used to perform sample training and data verification for groundwater data of Baodi and other four stations in recent five years. The results show that the classification accuracy of the decision tree algorithm for observing system interference and environmental interference events is above 80%. Based on a large number of accurate training samples, the decision tree algorithm can identify various water level interference events efficiently.