Abstract:【Purpose】In light of the frequent occurrence of disasters nowadays, the construction of urban resilience has once again attracted significant attention. As the fundamental living unit of a city, the community is of particular importance in enhancing resilience. Existing studies primarily rely on static assessment results as the basis for implementation and retrofitting, which fails to provide the optimal timing for risk early warnings and precise governance interventions within communities. 【Method】This research employs comparative analysis and inductive-deductive reasoning. From the perspective of resilience assessment and utilizing multi-source data as the methodology, it develops a framework for a dynamic monitoring and real-time assessment model of community resilience based on multi-source big data. 【Result】The model can effectively identify the dynamic changes in community resilience during both normal operations and emergency events. It is capable of pinpointing issues such as inadequate infrastructure capacity and problems arising from the concentration of high-risk populations, thereby validating the model's sensitivity and practicality across spatial and temporal dimensions. 【Conclusion】This framework can serve as an important tool for community renewal, providing a management instrument for enhancing the normalization and intelligence of community resilience.To enhance the quality of life for residents in the future and promote effective planning, efficient management and practical services in the community.