In view of the problem of real-time data acquisition errors in new energy plants, the data of new energy plants has mass and mutually coupled characteristics, a deep learning-based method for identifying and correcting bad data from new energy plants is proposed.Firstly, a LSTM identification model is constructed to identify the real-time bad data, and the bad data of the real-time identification is obtained. Secondly, the BP correction model optimized by the firefly algorithm is constructed to correct the bad data identified and obtain reliable data of the operation of the new energy station. The accuracy and effectiveness of the proposed method are verified by analyzing the real historical data of a typical wind farm.