Aiming at the batching problem in the cooperative antagonistic processes of multi-aircraft, a multi-aircraft collaborative batching method based on improved self-organizing iterative clustering is designed. This method addresses the problem of inconvenient and non intuitive manually parameter settings in traditional self-organizing iterative clustering algorithms, and is capable of providing reasonable batching results under the condition that a few intuitive hyperparameters were given. . Firstly, the high-dimensional multi-aircraft situational information is standardized and subjected to principal component analysis. Then, the density criterion from density clustering is introduced to enhance the merging and splitting operations in the traditional self-organizing iterative clustering method, thereby optimizing the hyperparameters involved in these operations. Finally, algorithm evaluation metrics are selected to assessment the cluster results under artificial data and real-world scenario data. Simulation results demonstrate that the improved self-organizing iterative clustering algorithm performs well both on artificial data and in real-world scenarios.