Guizhou Province ranks the fifth in China in terms of expressway mileage, yet the deployment of traffic meteorological stations remains sparse. Frequent low-visibility weather events, influenced by mountainous terrain, pose severe threats to road safety. Deep learning-based image recognition methods for visibility level classification assist in rapid assessment for traffic management during foggy conditions. However, conventional neural networks (CNNs) suffer from limited classification accuracy due to insufficient global feature extraction. To address this issue, this study integrates both local and global feature representations by constructing a hybrid network model, cTrans-Net, which combines CNNs and Transformer architectures. Surveillance video images from typical mountainous expressway sections in Guizhou are selected for model training and testing to achieve precise five-level visibility classification (L0-L4) that impacts traffic safety. Experimental results demonstrate that cTrans-Net achieves an overall accuracy of 89.17%, with an area under the curve (AUC) of 0.9822, outperforming several mainstream deep learning models. The overall accuracy of the independent validation set remains 87.75%. In evaluating low-visibility conditions (<500 m), which significantly affect traffic, the model attains the highest recognition recall of 90.66% for the most frequently occurring L2 level (100-200 m). For the less frequent L0 (>500 m) and L4 (≤50 m) categories, the recognition recall reaches 92.31% and 90.45%, respectively, indicating the model’s robustness in handling imbalanced datasets. Feature visualisation reveals that cTrans-Net effectively focuses on key regions such as road markings and fog distribution, which are critical for visibility assessment. This study provides a technical solution for fog-related visibility recognition in intelligent transportation systems, offering practical value for real-world applications in mountainous expressway environments. The proposed cTrans-Net demonstrates strong adaptability to imbalanced data scenarios and exhibits superior performance in critical low-visibility conditions, making it a viable tool for enhancing traffic safety management under adverse weather conditions.