Within the context of public health imitiatives, trails serve as critical infastruchue in public service systems, requiring planning thatintegrates multidimensional landscape information and user preferences. fowever, conventional methodologies stiuggle to conprehensively capture thre-dimensional (3D) trail landscape data and lack the capacity to quantify user preferences precisely. Whilebig data has significantly enhanced the accuracy and comprehensiveness of landscape perception research, its deep integration witlandscape design practice remains a urgent need. This study employs Beijing's Xiaoxishan area as a case shudy to establish a "DataAcquisition-Preference Evaluation-Decision Support" framework that integrates geospatial data with questionnaire surveys toaddress cumrent practical decision-making challenges, Big data-enabled landscape perception research demonstrates its capacity to(1)Provide multi-dimensional trail information.including fiequently used start/end points, photo-taking hotspots, trail networksand regional trail landscape information, revealing that umpaved trails dominates the natural network before paving (rock-dirt trails62.92%: dit trails: 22.90%): (2) Ouantify preference fulfllment levels aong three distinct user groups across the entire arearevealing that paving (39.44% paved trails)improves non-hiker satisfactionbut decreaseslong-distance hiker satisfaction due toaltered trail types;, (3) lnform spatial planming optimization. Based on preference-based disparities, spatial optimizatton strategiesare formulated, including restoration of high-preference natural trail types and development of user-specific trail typologies inlow-density network zones to achieve route difierentiation. This study confimns that multi-source data fusion eiectively addresesfoundational planning data scarcity and the challenge of preference localization during implementation.Fuitherore.it advancesthe translation of landscape perception into evidence-based design through the “information Acquisition-Demand Diagnosis-Decision Support", thereby establishing a robust scientifc foundation for trail development.