With the improvement of the Internet and connection technology, the data generated by sensors is gradually becoming complex. Deep learning methods have made great progress in anomaly detection of high-dimensional data. The graph deviation network (GDN) learns the relationship between sensor nodes to predict anomalies and has achieved certain results. Since the GDN model fails to deal with time dependence and instability of abnormal data, an external attention autoencoder based on GDN (AEEA-GDN) is proposed to deeply extract features. In addition, an adaptive learning mechanism is introduced during model training to help the network better adapt to changes in abnormal data. Experimental results on three real-world collected sensor datasets show that the AEEA-GDN model can more accurately detect anomalies than baseline methods and has better overall performance.