In the field of process mining, numerous process operations are highly dependent on accurate timestamp information in event logs. Consequently, quality issues related to timestamps have particularly significant impacts, especially identical timestamp errors, which can lead to misleading process insights and subsequently result in severe process deviations. Existing research addressing such errors lacks sufficient consideration of long-term dependencies between events and potential correlations between attributes, limiting the accuracy of repairing identical timestamp errors. To address this issue, a method for repairing identical timestamp errors based on a hierarchical Transformer model is proposed. This method captures long-distance behavioral dependencies between events and deep correlation information between attributes through hierarchical information propagation combined with multi-view interaction. The tasks of reordering erroneous events and predicting corresponding timestamps are accomplished layer by layer, thus effectively repairing event logs with identical timestamp errors. Evaluations conducted on four publicly available datasets demonstrate that the proposed method can effectively improve the accuracy of repairing identical timestamp errors.