The randomness of EV user charging behavior poses a great challenge to the short-term prediction of EV charging station charging load. There is a problem of the influence of multiple factors, and so a short-term prediction method of charging load of electric vehicle charging stations based on an improved sparrow search algorithm-convolutional neural network-gated recurrent neural network (ISSA-CNN-GRU) model is proposed. First, an input characteristic matrix containing three significant influencing factors of charging load, that is temperature, date type, and holiday, combined with historical charging load is constructed. Then, the advantages of a CNN in feature extraction, and data dimensionality reduction combined with a GRU neural network in time series prediction are used to build a CNN-GRU hybrid neural network model, and the ISSA algorithm based on hybrid strategy is used to optimize the hyperparameters of the hybrid neural network model. Finally, the input feature matrix in the optimized CNN-GRU model realizes the short-term prediction of charging load of a charging station. Taking the historical load data of charging stations in the public dataset of ANN-DATA in the United States as an actual example, compared with the random forest, CNN, GRU neural network, CNN-GRU model and CNN-GRU model with Bayesian optimization, particle swarm optimization and standard sparrow optimization algorithms respectively, the experimental results show that the proposed method offers a better prediction.