Abstract: In consideration of the on-station wagon operation anomaly situation, an anomaly detection algorithm of on-station wagon operation time (WOT) was proposed based on BIRCH-LKD. This algorithm was focusing on the WOT sequence and converting the sequence into the spherical cluster with WOT feature vector, without considering the peculiar form of the anomaly. A feature clustering tree of WOT feature vector was developed based on the classification rule to be utilized in the dominant anomaly detection and shortening survey range. After computing the K-distance of the other data object, according to the different variation, the appropriate sequence values were selected as the recessive anomaly. At last, the lower bound of WOT was excluded by using the median anomaly condition. The results show that the algorithm has a high detection rate. The anomaly of the WOT sequence can be identified quickly, and the accuracy can be more than 85%. The WOT sequence becomes more smooth and unbroken after the removal of the anomalies, and conforms to the actual development trend.