## 4 Conclusions and Discussion  
This study constructs a visibility analysis optimization algorithm to transform the abstract assessment of tourists' visual experience into quantitative data and graded visualization layers based on the landscape visibility. By utilizing influencing factors like the visible area, landscape water system distribution and the number of landscape resources, a landscape visual evaluation indicator system is established. Finally, by integrating the analytic hierarchy process with the visibility analysis optimization algorithm, an improved landscape visual evaluation method is proposed. In the case study conducted in Fenghuang Town, a graded display map (Figure 4) and chromatic map (Figure 5) of the landscape visual experience for the observation points show that the visibility analysis optimization algorithm not only can calculate the visible area but can also quantitatively analyze tourists' visual experience. The error analysis graph (Figure 15) indicates that compared to traditional evaluation methods based on tourists' subjective perception or expert scoring, the proposed method that quantitatively evaluates the landscape visual experience from an objective perspective can directly reflect tourists' actual visual experience effects, with evaluation results featuring higher accuracy, reliability, objectivity and scientificity.  
This method has great potential to develop into a relatively mature, more automated and scalable software tool in the future. It can not only assist in site selection and planning for sightseeing platforms but also support landscape planning and conservation in scenic areas. Regions with high landscape visual evaluation scores are considered to have a higher visual quality and should be included in protected area list. For regions with lower scores, planners can scientifically transform the landscape structure without damaging the environment to increase diversity, improve visual quality and attract more tourists.  
Of course, there are still some deficiencies in the research that need to be improved: many factors affect the landscape visual evaluation results, such as the landscape quality, weather factors and specific tourist-related factors. Therefore, the next plan is to establish a more comprehensive indicator system to quantitatively evaluate landscape visuals.  
**Author Contributions:** Supervision, Z.L., J.A., J.Z., H.S., Y.G., J.X., C.L. and G.M.-u.-d.; methodology, J.A. and Z.L.; software, J.A. and Z.L.; writing--review and editing. J.A.; project administration, Z.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by \"Key technology and integrated demonstration of living protection and utilization of traditional villages\", a national key research and development program project of the 13 th Five-Year Plan (No.:2019 YFD1100904), and the Shaanxi Province housing and urban/rural construction science and technology plan (No.:2020-K09).  
Figure 15: Results of the observation platform position error analysis.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Informed consent was obtained from all the subjects involved in the study.  
**Data Availability Statement:** Dataset available on request from the authors. The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Acknowledgments:** We thank [PERSON] for providing the PDERL algorithm to realize the viewshed analysis.  
**Conflicts of Interest:** The authors declare no conflicts of interest.