Predicting the time series of electromagnetic radiation in the environment is of great significance for public health protection and the adaptability of electronic devices to the electromagnetic environment. Aiming at the high volatility of the environmental electric field intensity time series， which leads to more outliers and interferes with model training， a Generalized Correlation entropy Loss function-based Transformer （GCL-Transformer） model was proposed. By applying nonlinear weighting to errors through kernel mapping， this model combined the gradient smoothing of Mean Square Error （MSE） with the outlier robustness of Mean Absolute Error （MAE）， effectively weakening the interference of outliers on the model training. Data were collected at three typical electromagnetic exposure monitoring sites in Beijing， and validation was carried out by multiple sets of cross-time scale prediction experiments. Comparisons were made with the traditional Transformer model， the variant model TOEformer （Temporal-Optimized Enhanced Transformer）， and the Long Short-Term Memory （LSTM） model. Experimental results indicate that GCL-Transformer model significantly outperforms the comparison models in terms of prediction accuracy. In short-term tasks with a prediction interval of one hour， the Root Mean Square Error （RMSE） of GCL-Transformer reaches 0.090 6 V/m， which is 30.6% lower than that of the traditional Transformer model （0.130 7 V/m）. Moreover， as the prediction interval extends to 72 hours， its error growth rate is the slowest （RMSE increases only from 0.090 6 V/m to 0.123 4 V/m）， demonstrating excellent long-term prediction stability.