Addressing the challenge of quantifying human emotional elements in urban renewal, this study constructs a research framework of "data collection - emotion recognition - urban renewal strategy" to explore human-centric renewal pathways driven by artificial intelligence technologies. By integrating large language models (LLMs) and knowledge graph technology, the research systematically synthesizes massive heterogeneous data from social media, resident hotlines, news supplement, and in-depth interviews. Taking Shanghai's North Sichuan Road neighborhood as a case study, it identifies eight categories of emotional spaces and conducts emotional scoring for 3 697 spatial data points. Key findings include: (1) There is a strong coupling relationship between emotional hotspots and physical urban fabric; (2) Nostalgia and joy collectively form the neighborhood's emotional foundation; (3) Negative emotional spaces exhibit functional mismatch-driven nodal clustering patterns. The study demonstrates that the synergistic application of multi-source data and AI technologies expands observational dimensions for emotional space analysis, providing a feasible approach to integrate technical tools with humanistic values in precision-oriented urban renewal.