It is difficult to predict accurately the bus load by traditional point prediction methods due to the violent fluctuation of some industrial bus load. A day-ahead interval prediction model of bus load based on the combination of convolutional neural network (CNN) and quantile regression long short-term memory (QRLSTM) is proposed in this paper. Firstly, the denoising auto-encoder is used to obtain the historical load data by de-noising the high frequency fluctuation of industrial load power. Then a one-dimensional CNN network encapsulated by time distribution layer is used to extract load features to improve the learning efficiency of the whole model. Finally, the QRLSTM model with attention mechanism is established for feature learning, and the load interval prediction results under different quantiles are calculated. The day-ahead interval prediction results of bus load are obtained by an industrial 220 kV bus and a commercial-residential 220 kV bus. The results show that the proposed prediction method has generally larger interval coverage, smaller interval mean width, smaller interval cumulative deviation and higher prediction effectiveness than the conventional quantile regression method.