Accurate prediction of short-term multiple energy loads is a prerequisite to ensure the reliable and efficient operation of integrated energy system. For this reason, a convolutional neural network-long short-term memory (CNN-LSTM) model for integrated energy system multivariate load prediction based on genetic algorithm particle swarm optimization (GAPSO) is proposed. Firstly, Pearson's coefficient is used to describe the correlation between the influencing factors and the load. Secondly, GAPSO algorithm is used to improve the LSTM model, and then a one-dimensional CNN is constructed to extract the hourly higher-order features, and the extracted implicit higher-order features are partitioned by the improved long short-term memory (LSTM) modeling. The multivariate load forecasting model based on GAPSO-CNN-LSTM for integrated energy system is constructed through quantile regression modeling. Finally, the load data of integrated energy system of Arizona State University Tempe Campus is used as an example, and the results show that the improved algorithm has a better convergence ability and the model has a higher prediction accuracy.