Accurate grasp of future electricity price information is of great significance to obtain the operation state of the market, support all parties involved in the market to make effective decisions, and promote market players to reasonably optimize resource allocation. Hence, a comprehensive model is constructed for day ahead price forecasting based on blending integrated learning mechanism. The model fully considers the high volatility of electricity price and adopts Ashin transform to reduce the impact of input data volatility on the prediction model. Four mature single electricity price forecasting models, SVM, Lightgbm, EWNN and SARIMAX are selected as primary learners to ensure the forecasting accuracy based on blending integrated learning model. The actual operation data of American PJM power market is selected to verify the above electricity price prediction model. The comparative analysis of the prediction results shows that the electricity price comprehensive prediction model based on blending integrated learning mechanism integrates the advantages of a variety of traditional prediction models and has good accuracy and stability.