###### Abstract
[15], such as shading, enhanced entrance/exit lighting, decorations, and sight-inducing systems. They concluded that the combination of sight-inducing strategies with improved lighting contributes to a coherent visual environment, effectively recycling light within tunnels, and optimizing safety and navigation while considering comfort and economy. More recently, [PERSON] et al. [10] embarked on a study in 2021, specifically focusing on visual comfort at the entrances and exits of urban underwater extra-long tunnels. Utilizing eye-tracking instruments, illuminance meters, and other experimental  
Figure 1: Casualies in different tunnels. apparatus, the research aimed to collect data on drivers' pupil areas and illuminance levels during tunnel traversal. The study identified the maximum transient velocity value of the pupil area as a critical metric for evaluating visual comfort at the entrance and exit sections of the East Lake Tunnel. Concurrently, in 2021, [PERSON] et al. [9] explored the comfort and safety evaluation of landscape design at highway tunnel entrances by leveraging eye movement indices. By employing the orthogonal experimental design method, they developed nine model scenarios influenced by four principal factors: entrance line shape, color, greenery, and the texture of the cave. Evaluation indices such as pupil diameter and gaze deviation time facilitated driving simulation experiments, which led to optimization suggestions for models posing driving safety hazards. Furthering this research trajectory, in 2022, [PERSON] et al. [12] examined the decoration of highway tunnel sidewalls from a driving comfort perspective by employing eye movement and heart rate indices as metrics for comfort and safety assessment. These experiments, conducted across three different landscape models, highlight an innovative approach to tunnel design evaluation. Nonetheless, it is important to note that these methodologies are not immune to subjective influences and exhibit limitations in the quantitative analysis of multivariate and multiparameter dimensions. With the advent of machine learning, a burgeoning number of researchers have turned to machine learning methodologies for the quantitative analysis and study of tunnel design and construction with the objective of boosting both the efficiency of tunnel construction and ensuring traffic safety. [PERSON] et al. [16] pioneered the application of machine learning in the construction of twin tunnels. Specifically, they employed multivariate adaptive regression splines and decision tree regression methods to forecast the maximum bending moment within the first tunnel lining, drawing on the constructed FE case studies. [PERSON] et al. [17] developed an advanced machine learning framework that integrates particle swarm optimization (PSO) with support vector regression (SVR), back-propagation neural network (BPNN), and extremum learning machine (ELM) to predict surface settlement during tunnel construction. Furthermore, [PERSON] et al. [18] constructed a prediction model for tunnel engineering monitoring using BPNN and MARS machine learning regression algorithms. In this setup, the residuals derived from the gray model are employed as the input data for the BP neural network. The result of this hybrid model serves as the predicted outcome, with a focus on monitoring data related to tunnel surface upper peripheral convergence and arch settlement deformation. Despite significant advancements in the field, machine learning methods have not yet been utilized to quantitatively analyze the rationality of tunnel cave landscape design. Current approaches to designing tunnel cave landscapes face challenges in adequately accounting for the safety and comfort of tunnel driving. Addressing this gap, this paper pioneers the use of machine learning to analyze tunnel cave landscape design from the perspective of drivers' physiological factors.