Considering the impact of time-of-use electricity pricing and charging utilization characteristics on the load of electric vehicle charging stations, a short-term load forecasting method is proposed for large scale electric vehicle charging stations that integrates long short-term memory and support vector regression (LSTM-SVR). Firstly, a feature matrix is constructed that includes factors influencing the charging load such as time-of-use electricity pricing, charging utilization rates, meteorological information, and historical data on charging load power. Secondly, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method is applied to decompose sequences of the feature matrix containing time-of-use electricity pricing and charging utilization rates, which enhances data diversity. This process also employs a combined correlation coefficient method for data reduction and feature selection. Subsequently, the northern goshawk optimization (NGO) algorithm is used to optimize the hyperparameters of both LSTM and SVR, solving for weight coefficients and constructing the integrated LSTM-SVR model. Finally, the model is validated using data from a large scale charging station in a specific city. Comparative experiments demonstrate that considering time-of-use electricity pricing and charging utilization features can effectively improve the accuracy of electric vehicle charging station load forecasting by over 8%. Moreover, the proposed LSTM-SVR forecasting method further enhances prediction accuracy.