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The evaluation of thermal comfort for railway passengers holds considerable importance, not only in reducing energy consumption but also in enhancing the passengers' experience. This paper presents a Transformer-based multi-task learning network (TransMTL) designed for railway passenger thermal comfort evaluation using EEG. We utilized manual features to extract temporal and frequency information, while a Transformer encoder distilled spatial information. The multi-task learning structure enhances model robustness by leveraging thermal comfort task correlations. We conducted experiments during winter and summer with high-speed railway passengers, establishing a comprehensive EEG dataset. The results demonstrated that our proposed EEG-TransMTL model outperformed classical machine learning and deep learning models in all four thermal comfort evaluation tasks, achieving accuracy rates of 65.00%, 66.70%, 80.38%, and 71.01%, respectively. We enhanced model interpretability by visualizing attention weights from the Transformer encoder, identifying key EEG channels. A simplified model utilizing only eight crucial channels also delivered notable performance. This research provides a practical and neuro-mechanism interpretable solution for thermal comfort evaluation. • The first EEG-based thermal comfort experiment in a train cabin was carried out. • A Transformer-based Multi-task Learning Network is proposed. • Our proposed method outperforms six baseline models in four tasks. • The crucial EEG channels are visualized and enhance the model's interpretability. [ABSTRACT FROM AUTHOR]