[Objective] In view of the impact of various extreme weather events on complex urban distribution networks，we propose an assessment method to identify vulnerable and susceptible points in distribution networks，especially electrical transformers with distribution functions that incur high outage losses. [Methods] First，we introduce a unified and parameterized integrated model of vulnerability. By employing kernel functions，cumulative transformations，and parameterized mappings to construct a unified framework，the model achieves a synergy between mechanism- and data-driven approaches，which makes it adaptable to diverse modeling scenarios. Second，the method fully considers the influence of land-use types on the operational environment and disaster risk of transformer equipment. We developed a vulnerability index system based on land-use categories by refining the vulnerability assessment from a spatial perspective. Additionally，we also propose a scenario probability sampling and reduction approach； by utilizing Monte Carlo sampling and sample-average approximation techniques，the failure probabilities computed by the model are converted into a set of failure scenarios to improve the efficiency and accuracy of failure scenario analysis. Finally，a simulation analysis was conducted on a 10 kV distribution network in a certain area of Beijing. [Results] The results demonstrate that the proposed model and method were able to identify vulnerabilities and susceptible points in the distribution network under various extreme weather conditions. Moreover，by deploying emergency power supply vehicles at susceptible points and positioning emergency repair resources at vulnerability points，indicators such as load loss and the number of affected users can be significantly improved. [Conclusions] The experimental results validate the effectiveness of the proposed approach in guiding power grid planning，construction，operation，and maintenance.