The randomness of EV users' charging behavior poses a great challenge to the short-term prediction of EV charging station charging load. Aiming at the problem of low short-term prediction accuracy of charging load of electric vehicle charging stations under the influence of multiple factors, a short-term prediction method of charging load of electric vehicle charging stations based on improved sparrow search algorithm-convolutional neural network-gated recurrent neural network (ISSA-CNN-GRU) model is proposed. Firstly, an input characteristic matrix containing three significant influencing factors of charging load, including temperature, date type, and holiday, and historical charging load is constructed. Then, the advantages of CNN in feature extraction, data dimensionality reduction and GRU neural network in time series prediction are combined 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 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 has better prediction effect.