Motion Attitude Prediction Algorithm Based on Data Mining and Intelligent Motion Training Model Construction

Abstract

Data mining refers to the process of obtaining information from a huge amount of data through algorithms, which provides reception support for people to apply data from simple problems to extracting and discovering knowledge in data. This study examines traditional data mining methods and their applications in order to increase the timeliness and usability of data extraction algorithms. Through data mining and recognition of motion gestures, the most accurate algorithm data are given. This study begins by looking at data mining classification methods and associated algorithms. Then, using a neural network, a motion attitude prediction is created and the neural network is used to test the algorithm. The experimental results show that the single-stage neural network and the bipolar neural network can achieve an average accuracy of 87.9%, while for the WTO model, it can achieve an average accuracy of 95.7%.