Abstract: As one of the important natural disasters faced by cities, urban floods have important impacts on urban social and economic development. For example, Beijing in 2012, Wuhan in 2016, and northern Anhui in 2018 suffered severe economic losses due to floods. This paper established the assessment index system of urban flood vulnerability based on the random forest model from nature, economy, and infrastructure aspects; then combined with the random forest and entropy weight method, the weights of the index system were calculated. Finally, the urban flood vulnerability assessment model (RF-VFS) based on the coupling of random forest (RF) and variable fuzzy set (VFS) was constructed. As a typical flood area in the middle and lower reaches of the Yangtze river basin, Nanjing city is prone to severe flood disasters in summer due to the influence of topography and seasonal rainfall. Therefore, Nanjing was taken as a case study area to explore the dynamic characteristics and driving factors of flood vulnerability, and the corresponding vulnerability grade curve of Nanjing from 1996 to 2018 is analyzed. The study handled data and its digital mapping based on GIS. The countermeasures were proposed from three aspects: urban flood management policies, urban infrastructure construction, and emergency measures for disaster prevention and reduction. The assessment results showed that indicators, such as accumulated rainfall during flood season, impervious surface area, GDP per square kilometer of land and urban road network density have significant positive correlation with flood. And the flood vulnerability level has been on the rise for the past 20 years, especially in the past 5 years, when the degree of vulnerability to flood has been relatively high. The results are consistent with the actual situation of flood vulnerability in Nanjing city. This study therefore provides decision-making recommendations for the relevant management departments to improve the flood management capability, and reduce the flood vulnerability.