In order to address the issue of metering inaccuracies in charging stations that directly affect the development of electric vehicles, a prediction method for the relative error of charging stations based on the ConvFormer model is proposed. First, charging station data is preprocessed using forward interpolation and normalization methods, and the dataset is transformed into a dataset of input relative errors. Then, a neural network with an improved unidirectional convolutional and attention combination for time-series forecasting is constructed, and common regression performance evaluation metrics, MAE and MSE, are selected for evaluation. Finally, based on seven days of charging station data, the relative error of charging stations for the next 24 hours is predicted, and compared to traditional Transformer and LSTM (Long Short-Term Memory) time-series models. The results show that the improved model yields the lowest values for both MAE and MSE, with a 57.05% reduction in MAE compared to the Transformer model and a 51.48% reduction compared to LSTM, and a 66.95% reduction in MSE compared to the Transformer model and approximately 62.23% reduction compared to LSTM.