Research on Badminton Technical and Tactical Decision-Making Based on Deep Learning Optimized Ant Colony Algorithm

With the increasing complexity of badminton, traditional methods for technical and tactical decision-making often struggle to handle high-frequency, high-complexity match scenarios. To address this issue, this study proposes a badminton decision-making method based on a deep learning optimized ant colony algorithm. First, the fundamental principles of the ant colony algorithm and its application in decision optimization are introduced, followed by an exploration of the advantages of deep learning in data processing and pattern recognition. A novel decision optimization framework is then designed by combining the strengths of deep learning and the ant colony algorithm. Experimental validation using real match data shows that the optimized algorithm improves decision accuracy and response speed while adapting to varying match environments, providing effective support for badminton decision-making. Finally, the study summarizes the innovations and limitations of this research and explores its potential application in other sports.