<p>With the development of smart devices and network technology, sports data statistics systems such as smart bracelets have also been widely studied, but there are fewer studies on racket sports data analysis systems such as badminton and other sports with complex movements, so this study will take badminton as the object of study, choose the inertial sensor-based data acquisition method to collect real-time data, choose a combination of sliding window and action window to carry out data segmentation, and use the principal component analysis is used to achieve data dimensionality reduction, and finally a two-layer classification algorithm based on support vector machine and adaptive boosting algorithm is established to study the six basic swinging and attacking actions of badminton under two types of grips. The test results show that the average recognition rate of the basic swinging movements under the two grips is 95.6 and 96.25%, respectively, and the overall recognition rate of the research model is 95.93%, which is 16.36% higher than that of the unimproved SVM algorithm, and the recognition rate of the research algorithm is the highest compared with many related algorithms. The experimental results show that the research algorithm is able to complete the recognition of badminton swing attack action, and the model algorithm has a higher recognition rate, which is of great value in the research of badminton attack action analysis.</p>