Electric vehicle charging load forecasting is crucial for ensuring the stable operation of the power grid.In view of the problems that the existing methods ignore the influence of multiple factors and have difficulty taking into account both long-term and short-term dependencies, this paper proposes a multi-feature input charging load prediction model combining Temporal Convolutional Networks (TCN) and Attention-enhanced Long Short-Term Memory (AttLSTM) neural network. The experimental results of three public datasets show that the prediction accuracy of this model is superior to that of the comparison model. The Mean Absolute Error (MAE) and Mean Squared Error (MSE) are reduced by 4.7% and 12.6% respectively compared with the optimal baseline model. This model can provide support for the stable operation of the power grid and the optimization of dispatching.