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Lightning disaster is a common natural disaster, which is affected by the randomness, instantaneity and regionality of its occurrence, so it is difficult to early-warning and forecast. In order to improve the accuracy and timeliness of lightning short-term and impending forecast, a new method based on clique clustering recognition and Kalman filter algorithm for lightning identification and tracking extrapolation is proposed. In the self-developed lightning monitoring and early warning system of Guangzhou City, the algorithm and the traditional Titan storm path algorithm are used to realize the lightning path prediction in the next hour by six minutes. By using the lightning location data in Guangzhou from May to October 2020, the two algorithms are tested and analyzed. The results show that the basic performance of the two algorithms is similar, and they can effectively identify, track and predict the thunderstorm moving path. Moreover, the optimized new algorithm is better than the traditional prediction algorithm based on Titan storm path in each time, which improves the accuracy of lightning proximity prediction has certain reference value.