To improve the ability of converter station operational and maintenance personnel to analyze massive generated events, this paper proposes an association rule mining analysis method considering a large number of events in converter stations. First, the entity features of recorded event and response logs are filtered using the original event tuple features. Then Boolean mapping and association mining modeling of the entity features of the converter station are performed. Then, an FP-Growth algorithm is improved using the mutual information (MI) principle and symmetric uncertainty (SU) theory. Finally, based on the improved algorithm, event correlation analysis of the converter station is carried out, and then the feedback of the converter station anomaly is carried out based on the results of the correlation rules. By mining the massive generated events during the commissioning of the Kun-Liu-Long DC converter station, it is shown that the proposed method can effectively extract strong correlation rules of judgment and result features from the massive events, discover the abnormal equipment actions of the converter station in time, and provide the decision support for operation and maintenance analysis. This work is supported by the National Natural Science Foundation of China (No. 51907084).