Meteorological decision-making is a crucial element in the meteorological disaster warning and prevention field. With the increasing frequency of meteorological disasters and the rapid development of edge intelligence, there is an urgent need to establish a meteorological early-warning platform that reduces human resource investment, decreases operating costs, and provides targeted information and response suggestions. Therefore, we propose the development of real-time decision-making based on edge intelligence-driven meteorological knowledge graph (EMKG), and aim to achieve meteorological emergency decision-making by combining the knowledge graph with edge intelligence. First, we collect data through edge devices and perform preprocessing and preliminary analysis on these devices to reduce the time and bandwidth requirements for data transmission to the cloud. Based on the above data, meteorological entity recognition and relation extraction were completed using techniques such as BERT, BiLSTM, CRF, and data augmentation. Then we trained a text generation model and deployed it on edge devices to achieve real-time meteorological decision-making. The experimental results show that EMKG effectively integrates edge intelligence and knowledge graph, and further improves the real-time and accuracy of meteorological decision-making.