The number and density of airspace decompositions in course prediction are often fixed, resulting in large evaluation errors or low search efficiency of sub-regions, which makes it difficult to take into account the accuracy and real-time performance of course prediction. Aiming at this problem, a model based on the dynamic divide and conquer in sub-regions has been built. Firstly, a new airspace decomposition rule is proposed to adaptively decompose according to the complexity of the environment. Secondly, a state-distance bivariate weight function is constructed. The weights of course prediction index system are dynamically adjusted to improve the evaluation reliability of the selection possibilities of subregions. Then, to realize a comprehensive course prediction, the quality function of the course sequence is constructed from length, tortuosity, and selection possibility. Finally, a heuristic function based on possibility factor and distance factor and a pheromone update rule based on positive and negative feedback mechanism are constructed to improve the convergence speed and quality of course prediction. Compared with the airspace uniform decomposition strategy, course evaluation quality function, and ant colony algorithm experiment, the proposed method has a faster convergence speed and a higher sequence quality.