Current sports competitions are mostly broadcast in the form of live video or video files, and information detection for athletes and sports economic processes can also be carried out through image detection technology. However, from the current situation, we can see that sports image detection technology is still immature. Therefore, this study uses sports video as a material to analyze the application of sports image detection technology. In this study, image detection technology edge detection, grayscale processing, object capture, target recognition, etc. are combined with the actual needs of sports video to achieve a variety of needs for sports image detection. Simultaneously, this study has realized the recognition of athletes, motion recognition, sports behavior judgment, etc. and built a test platform to verify the effectiveness of this research method. The results show that the research method has certain practicality and can provide a theoretical reference for subsequent related research.

In order to resist network malicious attacks, this paper briefly introduced the network intrusion detection model and K-means clustering analysis algorithm, improved them, and made a simulation analysis on two clustering analysis algorithms on MATLAB software. The results showed that the improved K-means algorithm could achieve central convergence faster in training, and the mean square deviation of clustering center was smaller than the traditional one in convergence. In the detection of normal and abnormal data, the improved K-means algorithm had higher accuracy and lower false alarm rate and missing report rate. In summary, the improved K-means algorithm can be applied to network intrusion detection.

The orthogonal triangular factorization (QRF) method is a widespread tool to calculate eigenvalues and has been used for many practical applications. However, as an emerging topic, only a few works have been devoted to handling dynamic QR factorization (DQRF). Moreover, the traditional methods for dynamic problems suffer from lagging errors and are susceptible to noise, thereby being unable to satisfy the requirements of the real-time solution. In this paper, a bounded adaptive function activated recurrent neural network (BAFARNN) is proposed to solve the DQRF with a faster convergence speed and enhance existing solution methods’ robustness. Theoretical analysis shows that the model can achieve global convergence in different environments. The results of the systematic experiment show that the BAFARNN model outperforms both the original ZNN (OZNN) model and the noise-tolerant zeroing neural network (NTZNN) model in terms of accuracy and convergence speed. This is true for both single constants and time-varying noise disturbances.