Abstract: In order to support the economic dispatch and optimal operation of an integrated energy distribution system, a comprehensive forecasting method of cooling, heating, and power loads based on deep learning is proposed. Firstly, the Pearson coefficient is used to quantitatively calculate the correlation between multiple loads and analyze the correlation between loads and influencing factors. Then, the structure of deep learning model based on convolutional neural network and support vector regression is introduced. The convolutional neural network is used as a feature extractor to extract more representative hidden feature information from input data, and the support vector regression is used as a prediction model to output prediction results. Meanwhile, missing data and outlier data are preprocessed. Finally, the actual data of an integrated energy system are used to verify the effectiveness of the algorithm, and the influences of multi-load correlation on the prediction results are compared and analyzed. The results show that the RCNN-SVR model proposed in this paper has good prediction accuracy for cooling, heating, and power loads. The research results can provide references for comprehensive load forecasting of integrated energy distribution system.