Risk assessment is a critical component of emergency management for public health emergencies. Conducting risk assessment based on quantum computing can significantly improve computational efficiency and accuracy. This study proposes a new method for urban public health emergency risk assessment by collecting multi-source geographic data and combining the Quantum Support Vector Machine (QSVM) algorithm, the Quantum Principal Component Analysis (QPCA) algorithm, and the natural breaks classification method. The feasibility of the model is validated through empirical analysis. The results indicate that the risk assessment model constructed using the QSVM algorithm performs excellently, effectively identifying and assessing the risk levels of public health emergencies in different urban areas, thus providing a scientific basis for emergency management.