Distributed low-carbon energy stations (DLCES) can improve energy utilization efficiency and renewable energy consumption rates. Accurate prediction of the future operating status of DLCES can ensure its safe and reliable operation. Therefore, a data-driven prediction method for the status of DLCES is proposed. Firstly, the structure and operating status of DLCES are analyzed, and the operating status is divided into normal, recovery, critical, and emergency states using key state variables and deviations. Secondly, a deep long-short term memory (LSTM) model is constructed, and an improved particle swarm optimization algorithm is used for hyper-parameter optimization to improve the performance of the prediction model. Finally, the CMPSO-LSTM model is simulated using test sets data, and the results are compared with those of RNN, LSTM, and BP neural networks. The results show that the CMPSO-LSTM model can improve prediction results and has more practical significance.