Aiming to address the problems of randomness and low prediction accuracy in electric vehicle charging load forecasting, the paper proposed a novel approach based on TCN. This approach integrated variational mode decomposition, attention mechanisms and multi-scale features, leading to the development of the electric vehicle load forecasting model, VMD-AM-MSF-TCNnet. Firstly, the proposed method utilizes variational mode decomposition, optimized using the whale optimization algorithm, to decompose the electric vehicle charging load sequence. Secondly, the method introduced a gating mechanism and dual attention mechanisms to enhance the residual blocks of the original TCN. The model achieved multi-scale feature fusion by combining the attention-enhanced outputs of improved TCN residual blocks of varying sizes. Finally, it finalized the prediction through the reconstruction of the load components. The experimental results indicate that the proposed model demonstrates improvements in the performance metrics of RSE、 RAE and R2 compared to the original TCN, showing that it has a good predictive performance.