The invention discloses a short-term power load prediction method and system based on a hybrid model. According to the method, the time sequence characteristics of the high-frequency component subsequences are extracted through the LSTM prediction model, the short-term power load is predicted in cooperation with the ELM-CATBOOST mixed prediction model composed of the CATBOOST prediction model and the first ELM prediction model, original power load data are decomposed into a plurality of intrinsic mode function components through the CEEMDAN decomposition algorithm, the model prediction difficulty is reduced, and the prediction efficiency is improved. The prediction accuracy is improved; besides, an LSTM prediction model is utilized to extract time sequence features of the high-frequency component subsequences, historical power load data and original power load data of the high-frequency component subsequences are combined to jointly serve as input features of an ELM-CATBOOST hybrid prediction model, input feature dimension information is greatly enriched, the advantages of a single model are integrated by using the ELM-CATBOOST hybrid prediction model, and the prediction accuracy is improved. The method has higher robustness and accuracy, and different input features and prediction models are adopted for high and low frequency component subsequences, so that the model complexity can be reduced.