An important task in business processes is anomaly detection of business process data which can be used to monitor and identify abnormal situations in enterprises or organizations. Objective Most current methods for business process anomaly detection only consider the control flow and do not consider the influence of other data attributes in event logs on business processes. Therefore an unsupervised anomaly detection model with multiple perspectives was proposed. Methods Firstly the control flow and data flow were processed separately and then spliced to form the input data type for the model. Secondly a model combining the self-attention mechanism and Bi-LSTM autoencoder was used to extract features from the perspectives of control flow and data flow of business process event logs respectively and then splicing was carried out for anomaly detection with the anomaly threshold determined by the reconstruction error of the autoencoder. Finally the proposed model was validated on public datasets. Results The proposed method was evaluated using real event logs and a comparative analysis with other methods showed that the proposed method performed better in three aspects precision recall and F1 score and the AUC of the proposed model reached large values on all datasets. Conclusion Experimental results show that the proposed method can better detect anomalies in process event logs. By incorporating attention mechanisms into the model and combining control flow and data flow perspectives a better representation of process data is achieved leading to significantly improved classification performance and clear advantages in business process anomaly detection.