The confined space and fluctuating brightness levels inside and outside highway tunnels result in notable disparities in driving behaviors across various sections. It's difficult to achieve differential management of various sections within tunnels due to the challenge of implementing uniform warning and control across the entire roadway. Based on the Tongji road trajectory sharing platform(TJRD TS), continuous microscopic parameters of vehicles were extracted to quantify driving characteristics using eight indicators. This approach was aimed at analyzing the differences in driving behavior and safety risks of vehicles at different tunnel locations. Based on unsupervised learning algorithms, a segmenting method was proposed for highway tunnel sections that considers driving characteristics. Firstly, principal components analysis (PCA) was employed to determine the main features representing driving behavior and traffic safety. Subsequently, the K-means clustering algorithm was utilized to divide the distribution of main features along the tunnel direction into segments. Finally, the rationality of tunnel section division was validated through significance analysis. The results show that the driving behavior and safety vary significantly at different positions within the tunnel. Based on driving characteristics, the tunnel sections are segmented into six parts using PCA-K-means clustering: approach section, entrance section, transition section, middle section, exit section, and departure section. The entrance and transition sections exhibit high variability in speed changes and unstable traffic flow, while conflict frequencies are high in the transition and exit sections, with vehicle deceleration and acceleration reaching peak values of 14.89% and 15.65%, respectively. The research results reveal the evolution pattern of vehicle driving characteristics within tunnels and facilitates effective segmentation of highway tunnels. The research results contribute to the formulation of proactive safety control strategies for tunnel vehicles and the realization of precise vehicle-road cooperative control.