In recent years, digital twin city platforms often encounter issues such as emphasizing the physical model’s accuracy over social cognition and specialized applications over a comprehensive data system, hindering the fulfilment of refined urban management’s real needs. Therefore, it is essential to define the characteristics of an urban management-oriented digital twin platform and construct a detailed evaluation mechanism. This study examines the framework for evaluating mapping rates, introducing three indicators: data resolution, data freshness, and data relevance. We developed a quantifiable and replicable evaluation model to assess data completeness, update timeliness, and network correlation degree. Using Shanghai’s Huamu digital twin platform as a case study, we calculated each indicator and formed a comprehensive mapping rate evaluation. This research achieves a quantitative analysis of digital twin city platforms’ development quality which was previously unmeasurable. Additionally, this study aids in advancing digital twin city platforms to facilitate the development of a “bottom-up” refined urban management approach.