To investigate driving workload on mountainous expressways, a naturalistic driving study was conducted utilizing an eye tracker to capture drivers' eye movement data in a realistic driving environment. Employing the change rate of pupil area, average saccade time, blink frequency, and fixation time ratio as primary indicators, a quantitative driving workload model was formulated through a combined weighting approach. This model aimed to reveal the driving workload evolution mechanism in typical scenarios of mountain expressways such as bridge and tunnel clusters, tunnel clusters, and short distances between tunnels and intersections. A clustering algorithm was applied to determine the classification thresholds for driving workload, thereby identifying high-risk scenarios characterized by heightened workload. The results show that the types of bridges within bridge-tunnel groups, the length of connection sections between tunnel groups, and the proximity of tunnels to interchanges significantly influence driving workload. A positive correlation was observed between driving workload and bridge size, whereas driving workload exhibited a negative correlation with the length of connection sections between tunnel groups and the distance from tunnels to interchanges. The thresholds of high, medium and low intensity levels of driving workload on mountain expressway are 0.54 and 0.26. Scenarios with bridge-tunnel groups composed of large or super-large bridges, tunnel groups with distances less than 300 m, and tunnel-to-interchange sections with distances less than 400 m were classified as high-risk driving workload scenarios. It is advisable to implement an intelligent lighting system within expressway tunnels, establish light-reducing structures at tunnel entrances, and in scenarios where tunnels are located in close proximity to interchanges, consider installing designated lane-changing zones within suitable tunnel sections to facilitate smooth lane transitions.