High-intensity urban development has emerged as a dominant paradigm in China''s rapid urbanization process, creating concentrated centers of population and economic activity while simultaneously generating significant environmental and social challenges. These urban areas face mounting pressures, including microclimate deterioration, urban heat island effects, and heightened stress on inhabitants'' psychological well-being. Urban park green spaces serve as crucial countermeasures to these challenges, providing essential ecological services, facilitating recreational activities, and contributing to mental health restoration. However, conventional planning approaches frequently prioritize physical infrastructure development over understanding nuanced human behavioral patterns, resulting in a fundamental disconnect between green space provision and actual user needs. The methodological framework incorporates diverse datasets, including anonymized mobile location data, real estate transaction records serving as socioeconomic indicators, points of interest distribution patterns, and detailed urban spatial information on park characteristics and land use configurations. The analytical process follows a five-phase approach: indicator selection based on demographic static attributes and spatiotemporal dynamic metrics, multi-source data integration with advanced geospatial techniques, indicator quantification via statistical classification, digital portrait generation through combinatorial analysis, and final extraction of prototypical user groups via dimensionality reduction. This robust analytical framework was implemented in an extensive empirical investigation of Nanjing''s central urban area, encompassing 808 square kilometers of high-intensity development. The study incorporated data from 294 distinct park green spaces, analyzed over 21,800 validated visitation records via sophisticated trajectory processing algorithms, and generated 488 unique digital portraits. Subsequent statistical analysis employing principal component analysis enabled the distillation of these portraits into six predominant typologies representing distinctive usage behaviors and preferences. The research findings reveal sophisticated multi-dimensional spatial and temporal patterns of green space utilization. Analysis demonstrates a pronounced core-periphery gradient in usage intensity that closely correlates with established population density distributions. While large comprehensive parks attract the greatest absolute number of visitors due to their substantial size and diverse amenities, smaller neighborhood parks and pocket green spaces exhibit remarkably higher usage density and service efficiency, underscoring their vital role in serving high-density urban communities. Temporal analysis identifies distinct diurnal and weekly patterns with pronounced peaks during afternoon hours and weekend periods, while quantitative assessment confirms that shortdistance movements and brief duration visits constitute the dominant mode of park utilization. Advanced spatial statistical analysis through geographically weighted regression modeling identified key determinants influencing park usage patterns. The results indicate that green space area, cultural and scenic attraction density, transportation infrastructure availability, and recreational facility concentration positively correlate with increased user attendance. Conversely, residential land area extent and occupational point density demonstrate negative correlations with park usage intensity. Based on these empirical insights, the study proposes a comprehensive dual-strategy optimization framework integrating spatial configuration enhancements with targeted facility improvements. Specific recommendations include strategic implantation of pocket parks in high-density residential zones, development of quality greenways along major transportation corridors, enhancement of community parks for social interaction, and creation of specialized park facilities in peripheral urban areas. This research provides a sophisticated data-driven framework for transitioning urban green space planning from traditional supply-oriented approaches to innovative demand-responsive strategies. The digital portrait methodology facilitates detailed understanding of urban humanenvironment interactions, informs equitable, efficient and sustainable urban green space development, and effectively meets residents'' needs under high-intensity developmen