The short-term electric load forecasting is featured with 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 electric load forecasting and extract information from it, this 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 frequency domain characteristics of data. 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 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 prediction. The load data from a domestic company 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-term memory (LSTM), temporal convolutional network (TCN), and others, achieves a higher level of predictive accuracy.