Innovative small and medium-sized enterprises (SMEs) encounter unique challenges in accessing critical resources and strengthening their economic resilience. This study systematically examines the spatial distribution and location selection mechanisms of representative innovative SMEs, with the aim of uncovering the decision-making logic and key factors influencing their site selection strategies. Employing advanced big data collection techniques, the research develops refined quantitative analysis models and integrates Geographic Information System (GIS) neighborhood density analysis with the Spearman correlation coefficient method. The findings shed light on the locational preferences and distribution patterns of innovative SMEs within urban environments. The results reveal a pronounced divergence in spatial preferences: high-tech enterprises gravitate toward proximity to innovation hubs, research institutions, and financial services, leveraging knowledge spillovers and technological support, whereas startups prioritize regional economic vitality and living convenience to meet their early-stage requirements for funding and talent. This study offers a fresh theoretical perspective on the spatial selection behaviors of innovative SMEs in central urban areas, providing robust empirical evidence and actionable insights to inform policy formulation and urban planning in Qingdao and other comparable cities.