Electric vehicle charging load forecasting supports power dispatch decisions by addressing load fluctuations from widespread EV grid integration. A new method for predicting short-term EV charging loads is proposed to enhance power grid stability and reliability by improving load forecasting accuracy. First, historical load data is decomposed into subcomponents using VMD, then combined with temperature data and input into multiple TCN-LSTM branches for feature extraction, simplifying EV load sequence complexity. Secondly, a two-stage attention mechanism enhances the LSTM structure, improving load characteristic capture at specific times and feature dimension fusion, boosting complex load pattern recognition. Finally, a time conversion prediction module integrates results via a fully connected layer to enhance prediction accuracy and reduce errors. Case study analyzes real EV charging station load data from a Shaoxing community. Experimental results show the proposed method reduces MSE by 68%, MAE by 60%, and improves the performance index by 4%, demonstrating strong predictive performance.