Under the background of healthy cities and stock renewal, functional lighting plays an important role in ensuring nighttime outdoor activities, promoting mood and health. Therefore, it is necessary to explore the examination works and systematically sort out the evaluation methods of functional lighting. At present, the evaluation of functional lighting at the medium and macro scales is mostly based on summarizing micro node conclusions, which is difficult to fully connect with the spatial layout, business distribution, public behavior needs, and potential of urban lighting. Compared to complex urban environments (including multiple influencing variables), university campus, due to its highly introverted and autonomous characteristics, could provide a relatively ideal and simplified environment for the refinement of functional lighting evaluation methods, which could facilitate more efficient discussions on optimizing evaluation methods and provide support for subsequent research on functional lighting evaluation in complex urban environments.This study uses multi-source data methods to evaluate roads and open spaces in campus by obtaining users’ cognition of the nighttime environment through online questionnaires, creating a cognitive map based on campus night perception through subjective evaluation and on-site interviews, measuring the minimum illuminance and illuminance uniformity using a Konica Minolta T-10 illuminance meter to quantitatively evaluate the campus functional lighting by comparing it with industry standards. And Perform Kernel density analysis using Points of Interest (POI) were used to identify potential heat zones in campuses under different POI types, then campus hot zone identification and verification were completed through drone (Dji air3) imaging. Conclusions received are as follows. On an overall level, the campus functional lighting is insufficient and difficult to meet the needs of daily outdoor activities. At the level of management and maintenance, there are widespread problems such as roads without lights, lights not turning on, and light source attenuation, corresponding maintenance work should be optimized. At the level of behavioral characteristics, a. the dormitory area is an important factor affecting evening vitality, and there is an urgent need to improve the activity path and venue lighting centered on the dormitory area in the future; b. The student prefer open spaces with better lighting quality, and attention should be paid to the improvement of functional lighting in such places. At the level of venue potential, a. Numerous open spaces on campus have (varying degrees of) nighttime activity potential, but the current low-quality lighting has had a negative impact on this. Improving lighting quality to promote campus nighttime public life is an important aspect of future work; b. The insufficient functional lighting has led to the longterm vacancy of open spaces and the neglect of such spaces during maintenance, resulting in significant waste of public spaces in terms of site utilization and cultural promotion, which should be given attention from both management and maintenance aspects. Regarding research methods Comparing traditional methods (questionnaire + cognitive map + indicator measurement) with optimization methods (POI + drone). In terms of basic problem analysis, both methods are applicable. The weak points in lighting obtained by traditional methods can be effectively identified through optimization methods, indicating the feasibility of identifying micro objects or features through optimization methods, which can more efficiently complete the research and analysis of medium and large-scale objects. In terms of the correlation between campus business formats and nighttime vitality, the advantage of traditional methods lies in their ability to effectively identify detailed differences between different regions, while optimization methods can establish spatial connections between regions (such as paths, scales, etc.),which is of great significance in subsequent mid and macro level researches. In the evaluation of micro objects, traditional methods can intuitively obtain the lighting changes inside the field and the perceptual changes caused by them, which has advantages in micro research. Optimization methods are better at discovering the distribution of similar problems within the scope and drawing common conclusions. This indicates that both methods are effective in determining basic problems, typical objects, etc. However, in terms of spatial layout, path connections, and common problem sorting, the optimization method can significantly improve work efficiency by leveraging its scale advantage to draw systematic conclusions simultaneously. In future similar evaluation work, by combining the two, a multi-source data evaluation method that takes into account both macro and micro needs will be formed, which will provide effective support for the development of lighting evaluation work.