Hindawi Complexity Volume 2020, Article ID 8877161, 15 pages https://doi.org/10.1155/2020/8877161 Research Article Analysis Technology of Tennis Sports Match Based on Data Mining and Image Feature Retrieval Hong Huang and Risheng Deng School of Sports Science, Lingnan Normal University, Zhanjiang, Guangdong 524048, China Correspondence should be addressed to Risheng Deng;
[email protected] Received 3 September 2020; Revised 25 September 2020; Accepted 30 September 2020; Published 14 October 2020 Academic Editor: Chuan Lin Copyright © 2020 Hong Huang and Risheng Deng. )is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Tennis game technical analysis is affected by factors such as complex background and on-site noise, which will lead to certain deviations in the results, and it is difficult to obtain scientific and effective tennis technical training strategies through a few game videos. In order to improve the performance of tennis game technical analysis, based on machine learning algorithms, this paper combines image analysis to identify athletes’ movement characteristics and image feature recognition processing with image recognition technology, realizes real-time tracking of athletes’ dynamic characteristics, and records technical characteristics. Moreover, this paper combines data mining technology to obtain effective data from massive video and image data, uses mathematical statistics and data mining technology for data processing, and scientifically analyzes tennis game technology with the support of ergonomics. In addition, this paper designs a controlled experiment to verify the technical analysis effect of the tennis match and the performance of the model itself.