In response to the multidimensional evaluation needs of intelligent park management efficiency

this article constructs a comprehensive evaluation system for intelligent park management with 4 primary indicators and 12 secondary indicators. The systematic weight allocation advantage of Analytic Hierarchy Process (AHP) is combined with the uncertainty handling ability of Fuzzy Comprehensive Evaluation (FCE). By establishing a dynamic membership function and improving the synthesis operator

a deep integration of qualitative analysis and quantitative calculation is achieved. Using a certain intelligent park as the empirical object

it evaluates three solutions: traditional management

preliminary intelligence

and deep intelligence. The results show that the deep intelligence scheme is significantly better than other schemes in terms of infrastructure and technological innovation

with a comprehensive evaluation value of 0.4103. The research results have important theoretical value and practical significance for improving the management efficiency of smart parks and optimizing resource allocation.