State Grid Corporation of China has researched and released dual-mode communication technology standards based on High Speed Power Line Communication (HPLC) and Radio Frequency (RF) for collecting electricity consumption data in low-voltage distribution metering networks. Due to occasional unknown changes, the topology of the low-voltage distribution metering network has changed, and some nodes cannot even be used. In this paper, we utilized attention mechanisms to accurately locate key features and combined them with Long Short-Term Memory (LSTM) networks to reveal long-term dependencies in the data. Based on this innovative approach, we propose the AT-LSTM model, which is an efficient detection method designed specifically for time series data Unlike traditional anomaly detection algorithms, it can detect and locate node channel anomalies in dual-mode communication networks without any relevant domain knowledge, combined with identified communication network topology, and predict dual-mode channel state information at any given time point based on historical data. Realize topology change recognition and communication anomaly detection when the prediction error follows an approximate Gaussian distribution. The effectiveness of the model proposed in this paper was demonstrated through evaluation using synthetic data extracted from low-voltage distribution networks.