s: The short-term electricity load forecasting exhibits characteristics such as nonlinearity, periodicity, and rapid changes. Therefore, it requires a powerful model to effectively extract information from it. In order to enhance the accuracy of short-term electricity load forecasting and extract information from it, the paper proposes a predictive approach that integrates Discrete Cosine Transform (DCT), Convolutional Neural Network (CNN), and Gated Recurrent Unit (GRU) in a hybrid model. The model first employs the DCT transformation to convert time-domain information into frequency-domain information, which aids in capturing the data's frequency domain characteristics. Subsequently, the data, which contains both time-domain and frequency-domain information, is input into convolutional neural networks and gated recurrent units for training and prediction. In the model, data containing both time-domain and frequency-domain information is first subjected to feature extraction using convolutional neural networks. The data is then passed to gated recurrent units (GRU) to fully leverage the recurrent nature of GRU, allowing the model to learn the periodic and temporal characteristics of the data and achieve more accurate predictions. The load data from Nongfu Spring and California, USA, are used in this work as examples for experimental verification. The experimental results demonstrate that the proposed hybrid model, in comparison to traditional methods such as GRU neural networks, Long-Short Time Memory (LSTM), Temporal Convolutional Network (TCN), and others, achieves a higher level of predictive accuracy.