In close air combat, it is very important to get a piece of reliable air combat situation information in time for decisionmaking guidance. Aimed at the problems that there are drastic changes in the situation of close air combat and multidimensional coupling of evaluation parameters, a multielement air combat situation segmentation clustering method based on LKshapeHACA is proposed. Taking the hierarchical time series cluster analysis as a framework, the number of clusters is determined by using the Laplace centrality method, and the cluster analysis on multivariate time series is made by using Kshape, solving the problem of situation information extraction under multidimensional parameters. The test is performed by using the 12 sets of close air combat data, and the 14 clustering algorithms are compared. The results show that LKshapeHACA is more consistent with the actual air combat situation in the accuracy of cluster center determination and situation segmentation.