Development of online sports guiding assurance system based on artificial fish swarm algorithm and intelligent speech semantic extraction

Dongning Kang ()
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Dongning Kang: Shijiazhuang University of Applied Technology

International Journal of System Assurance Engineering and Management, 2025, vol. 16, issue 3, No 5, 939-948

Abstract: Abstract Under the recent condition of information technology, intelligent sports teaching management needs to change the traditional mode to improve the efficiency. At present, modern multimedia technology has fully integrated network communication technology, and teaching work is more carried out in the network conditions, which makes the teaching mode completely changed. Because of characteristics of the autonomous and interactive, network teaching mode makes the communication between students, students and teachers easier and closer. The traditional single auxiliary teaching mode can not quickly feedback various information defects are also solved by the network teaching mode. The network teaching mode has own advantages. It can not only fully mobilize students' enthusiasm for learning, but also change the traditional teaching mode. In this paper, we propose the online sports guiding assurance system based on artificial intelligence algorithm and intelligent speech semantic extraction. The objectives of the study contains the following aspects: (1) AFSA algorithm has the advantages of good global convergence, low initial value requirements, strong robustness and so on. So we consider this as the model for the AI. (2) In B/S multi-layer structure, the hierarchy is divided not based on physical structure but according to its structural logic, so we consider this as the platform structure. The experimental results show that the proposed algorithm performs well in error correction, duplication processing and data integration in database text matching. The proposed model performs best on each test set, and its accuracy is generally higher than that of traditional algorithms such as SVM, CNN, KNN and FCM. On the test set, the accuracy of the proposed method is 0.9896, while the accuracy of SVM, CNN, KNN and FCM are 0.9175, 0.9235, 0.9389 and 0.9564 respectively.

Keywords: Artificial intelligence (AI); Genetic algorithm; Semantic analysis; Network teaching; Physical guiding; Artificial fish swarm algorithm (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s13198-025-02718-3

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