Discrete Dynamic Modeling Analysis of Badminton Games Based on Viterbi Algorithm in College Badminton Physical Education

With the rapid development of machine learning and artificial intelligence, society has gradually stepped into the era of intelligence, and a series of research results and intelligent products based on machine learning and artificial intelligence have emerged. In this paper, machine learning methods are applied to the classification and recognition of badminton stroke actions, and on this basis, a statistical scheme of badminton technical features is constructed, a badminton stroke action recognition algorithm is proposed, and a real-time badminton action recognition system is implemented on this basis. Based on the model features of Hidden Markov Model (HMM) and the training method of Viterbi algorithm (VA), this paper proposes a VA scheme to recognize ten common badminton strokes. Experiments show that the algorithm model system designed in this paper can recognize the ten common strokes in real time. The average recognition rate of the proposed scheme is improved by 6.4% compared with the traditional scheme, and the final comprehensive recognition rate of the stroke can reach 94%.