Short-term load forecasting plays an important role in power system planning and operation. A hybrid short-term load probability density forecasting method based on convolutional bi-directional long short-term memory quantile regression blending attention mechanism is proposed. Firstly, the weather variables and historical loads are selected by using relevant mechanisms.Next, the Copula model is used to calculate the risk threshold,which is used to construct the peak binary indicator input characteristics. Then, the selected feature sets are input into the convolutional bi-directional long short-term memory quantile regression blending attention mechanism prediction model. Then, kernel density estimation is used to fit the load probabilistic prediction. Finally, the prediction performance is evaluated using the mean absolute percentage error and root mean square error. The simulation results show that proposed model has higher prediction accuracy.