Aiming at the problem of low recognition accuracy due to the increasing complexity of aircraft flight movements in air combat， this paper proposes an air combat flight situation recognition method based on enhanced support vector regression. The sparrow search algorithm is improved by using pinhole imaging and chaos initialization. The improved sparrow algorithm is used to optimize the support vector regression algorithm， which is specifically represented by the optimisation of the parameters of the Gaussian kernel function in the support vector regression algorithm. The optimized support vector regression algorithm is used to identify aircraft movements.Five basic flight actions and complex flight actions are used to verify the recognition accuracy of the method. Simulation shows that the optimised support vector regression algorithm improves the average recognition rate of basic flight manoeuvres by at least 2.2%， and the average recognition rate of complex flight action by at least 3.7%， compared with the traditional support vector regression algorithm， fuzzy support vector machine algorithm， traditional clustering algorithm， and neural network algorithm.