The oil and gas station is an important part of the transportation and storage system of oil and natural gas in China. The safe and stable operation of the oil and gas station is an important part of the oil and gas system and even the national economic development. China has a vast territory with diverse climate types, including storm, lightning, typhoon and other meteorological disasters. When meteorological disaster occurs in and around oil and gas stations and yards, it may cause damage to relevant installations, leading to abnormal operation of stations and yards, and even accidents. The medium in the oil and gas stations usually has significant characteristics of flammability and explosion. In the common situations, petroleum and related liquid processing products are generally volatility and easy to form flammable gas clouds. Natural gas stations are often accompanied by high pressure operation conditions. Once abnormal working condition or accident occurs, the life safety of staff inside the station area and the surrounding environment will bear risks. That’s difficult to avoid totally meteorological disasters, but targeted safety measures can be taken to strengthen the stability of stations to resist meteorological disasters and reduce the loss. Here, considering the equipment of oil and gas station often has more oil and gas leakage, explosion and other accidents when encountering meteorological disasters such as rainstorm and thunderstorm, this paper proposed a quantitative vulnerable node identification method according to the occurrence scenario of meteorological disasters and the possible disaster evolution path in related scenarios. The methods often adopted at present lack fineness and accuracy in identifying vulnerable nodes of station equipment, which may cost a lot to protect the wrong nodes and vulnerable nodes, but still lead to leakage of storage tank pipelines, or even high risk of explosion. Based on the improvement of gravity model and the Leuven algorithm, this paper proposed a method to identify vulnerable nodes of equipment in community analysis model. This paper revealed five vulnerable nodes, including automatic fire extinguishing system, ranking highest in rainstorm, and five vulnerable nodes, including lightning rod, ranking highest in thunderstorm. Compared with other analysis methods, the precision of community analysis model increased by more than 10%, the accuracy of analysis results increased by more than 10%, and the recall rate increased by more than 14%. The fragile nodes in historical cases can be identified, and more vulnerable nodes can be identified.