Decision information for process monitoring and management can be obtained by analyzing and predicting the event log of the business process. The existing research methods are mostly targeted at specific single-task prediction, and the portability between different task prediction methods is not high. Through multitask prediction, information can be shared among multiple tasks, improving the single-task prediction accuracy. However, the multitask prediction effect of existing research on repetitive activities must be improved. Based on the aforementioned problems, we propose a depth neural network model combining the attention mechanism and bidirectional long-short term memory, achieving multitask prediction for repetitive activities and time associated with the business process. The proposed prediction model can share the learned feature representation of different tasks and achieve multitask parallel training. Comparison is performed by applying different methods on datasets. The obtained results demonstrate that the proposed method improves the prediction efficiency and accuracy, especially in case of repetitive activities.