Public health emergencies not only affect the characteristics of urban crowd aggregation but also lead to significant changes in emotional responses. However， current research tends to focus either on crowd aggregation characteristics or emotional responses in isolation， lacking comprehensive analysis. To address this gap， this study proposes an integrated monitoring framework that combines crowd aggregation features with emotional responses to explore the dual impact of public health events on urban psychosocial behavior. The study focuses on the main urban area of Xi’an and utilizes Baidu heat maps to calculate the crowd aggregation index and tidal index. It also integrates social media data for sentiment analysis， employing a social semantic network to reveal the main causes of emotional responses. Data analysis covers both the pandemic control period and the recovery period， comparing crowd aggregation characteristics and emotional responses using multi-source heterogeneous big data. The results indicate that the spatial intensity of crowd aggregation in Xi’an differs significantly between the pandemic control and recovery periods， with higher crowd aggregation during the recovery period. Additionally， the scope and intensity of positive emotions increased during the recovery period compared to the control period， while negative emotions were alleviated. Furthermore， the social semantic network analysis reveals a strong correlation between public sentiment and pandemic control policies. The framework developed in this study not only provides new insights into the analysis of crowd aggregation and emotions under public health events but also offers data support for government decision-making in pandemic control and emotional management. This research holds strong practical value， promoting the integration and application of multi-source spatiotemporal big data in public health and social digital governance.