With the development of smart power grids, electronic transformers have been widely used to monitor the online status of power grids. However, electronic transformers have the drawback of poor long-term stability, leading to a requirement for frequent measurement. Aiming to monitor the online status frequently and conveniently, we proposed an attention mechanism-optimized Seq2Seq network to predict the error state of transformers, which combines an attention mechanism, Seq2Seq network, and bidirectional long short-term memory networks to mine the sequential information from online monitoring data of electronic transformers. We implemented the proposed method on the monitoring data of electronic transformers in a certain electric field. Experiments showed that our proposed attention mechanism-optimized Seq2Seq network has high accuracy in the aspect of error prediction.