The accurate prediction of spot market electricity prices plays a crucial role in protecting the interests of participants in the electricity market. Both raw material prices and climate factors can affect the fluctuation of electricity prices in the spot market. In addition, a large amount of wind and solar energy is currently involved in spot market transactions, making electricity price forecasting in the spot market more challenging. Therefore, a spot market electricity price prediction model that integrates CEEMDAN, BERT, and LSTM is proposed. Firstly, the CEEMDAN algorithm is used to decompose the original electricity price data；Subsequently, the BERT algorithm is used to process the text data of three exogenous features：raw material prices, climate conditions, and renewable energy, in order to improve the prediction accuracy of the model；Next, the electricity price decomposition subsequence is combined with the results of exogenous feature processing, and LSTM is used to predict the model. The predicted results are then overlaid to obtain the final electricity price. Finally, the effectiveness of the proposed method was verified through simulation, and the results show that the CEEMDAN-BERT-LSTM prediction model improved the accuracy of electricity price prediction significantly.