The extraction of urban functional areas plays a critical role in data-driven policymaking. While previous studies have primarily focused on general-purpose functional area extraction, this study proposes a novel methodology for identifying bicycle-specialized functional areas by integrating spatial network analysis with semantic POI embedding. Using Seoul, South Korea, as a case study, we first constructed linear spatial units by applying a network Voronoi algorithm to the city’s bicycle road network and public bicycle station data. Next, POI data were classified according to bicycle trip purposes and embedded using a Word2Vec-based approach to capture high-dimensional semantic features. These features were then aggregated for each spatial unit, with weights assigned based on POI type and proximity to bicycle roads. Finally, K-means clustering was conducted to extract distinct functional areas optimized for bicycle usage. The experimental results identified four unique cluster types, including residential-centered and park-oriented zones, demonstrating the effectiveness of the proposed methodology in supporting bicycle-friendly urban planning. This approach offers valuable insights for public bicycle redistribution, infrastructure deployment, and sustainable mobility policy.