## 1 Introduction
This approach involves considering geospatial, population, economic, cultural, transportation, infrastructure, and industrial aspects [33; 34]. Researchers have selected pertinent indicators and analyzed the driving forces and influencing mechanisms of UA challenges through qualitative analysis [35; 36], spatial econometric modeling [37], negative binomial regression, QAP correlation analysis, and similar methodologies. In the context of complex network analysis applied to UA network structure research, the variables involved are relational data, which may exhibit multicollinearity, making it challenging to ascertain whether the disturbance term conforms to a normal distribution. The QAP analysis method, a non-parametric matrix processing approach, alleviates the need for considering variable independence and effectively addresses multicollinearity [38; 39]. Consequently, QAP analysis is widely employed to examine the factors influencing the network structure of UAs [40; 41]. In recent years, the use of urban data has provided a practical basis for understanding the dynamics of UAs [37; 42; 43]. Previous scholarly investigations into the network structure of UAs primarily relied on the analysis and exploration of traditional static datasets encompassing economic, transportation, and population facets, with a focus on sources such as highway passenger flow data, highway network data, and census data [2; 3; 44]. However, these datasets posed challenges due to their substantial volume, limited precision, and the capability to only reflect network characteristics over extended periods. In recent years, major Internet companies like Baidu, Tencent, and microblogging platforms have introduced location service data with real-time updates, offering a novel approach through location-based big data for characterizing UA network structures [45; 46]. Concurrently, population migration big data, with their ample sample size and the inclusion of migration scale and direction data, is better suited for characterizing the scale and flow direction of inter-city network connections to a considerable extent. Consequently, Baidu migration data have been increasingly used by researchers to construct population migration networks, thereby enhancing the analysis of inter-city network structures [21; 47]. In light of these considerations, this study investigates the impact of the COVID-19 pandemic on population flow networks in 19 urban agglomerations (UAs) across China, utilizing Baidu migration big data and complex network analysis techniques. The research delves into a classification-based examination of China's UA networks under the COVID-19 context, taking into account both the entire temporal span and a long-term perspective. Furthermore, it investigates the stability characteristics of diverse types of UA networks in China at distinct phases of COVID-19, elucidating their evolutionary patterns. Additionally,this study employs QAP correlation analysis to identify the factors influencing the network structure of these nineteen UAs. These factors include economic development levels, medical and transportation facilities, spatial proximity, and innovation and openness. Comparative analyses are conducted to discern the impact of these factors on UA network structure stability, both before and after COVID-19. The objective is to provide insights into how these factors may influence UA network stability in future scenarios. This research aims to offer valuable references for UA planning, positioning, resource allocation, and policy formulation in the face of future public health emergencies.