Abstract: Accurate day-ahead electricity price prediction is the basis for market operation and policy planning, while market disclosure information is essential for electricity price prediction. This paper proposes a combined CNN-GRU deep-learning electricity price prediction model introducing the Self-attention mechanism. Firstly, for the trading process of the Shanxi electricity spot market and the formation mechanism of electricity price before the day, it is proposed to adopt the maximum mutual information coefficient method to extract features from the market disclosed information data such as boundary conditions before the day, to determine the key influencing factors of electricity price and its weight coefficients; and then based on the weighted grey correlation of the history of similar day screening method to generate the historical dataset of electricity price prediction, and to excavate the internal change rule of electricity price and its features; Finally, the CNN-GRU model introducing Self-attention mechanism is used to get the predicted electricity price based on the historical data set. The effectiveness and accuracy of the prediction method proposed in this paper are verified through examples.