The exercise volume and exercise level can be quantitatively assessed by measuring and collecting athletes’ health and exercise data. The protection of athletes’ health information has lately become an important research topic due to a rise in sports activities. However, due to the nature of the data and the limits of protection models, protecting athlete health data is a complex undertaking. Machine learning and blockchain have caused worldwide technological innovation, and it is bound to bring deep modifications to the sports industry. The main purpose of blockchain is security, decentralization, traceability, and credibility of the athlete’s health data protection and gathering system. To progress and increase the sports industry and methodically assess the physical fitness of sportspersons’ health information, this study concentrates on the Machine Learning and Blockchain-based Athlete Health Information Protection System (MLB-AHIPS) proposed in the sports industry. The ML technique is utilized to clean and handle the information to comprehend the recognition and secure managing of the sportsperson’s fitness information. The system uses attribute-based access control, which permits dynamic and fine-grained access to athlete health data, and then stores the health data in the blockchain, which can be secured and tamper-proof by expressing the respective smart contracts. The simulation outcomes illustrate that the suggested MLB-AHIPS attains a high accuracy ratio of 97.8%, security ratio of 98.3%, an efficiency ratio of 97.1%, scalability ratio of 98.9%, and data access rate of 97.2% compared to other existing approach.

This study aims to discuss the application value of KMC algorithm optimized by heuristic method in basketball big data analysis and visual management. Because the data in basketball big data is too complicated and incomplete, the extraction of information is not direct and effective enough. Based on the metaheuristic K-Means clustering (KMC) algorithm, the weights and genetic algorithm are introduced to optimize it, and the University of California at Irvine (UCI) data set is applied to analyze the big data clustering performance of the optimized KMC algorithm. The 2018-2019 season National Basketball Association (NBA) shooting guards are selected as the research objects, and the optimized KMC algorithm is used to process the data and analyze the NBA scoring functional factors. It is found that the number of clusters increased from 2 to 16. After optimization, the Between-Within Proportion (BWP) value of the KMC algorithm only drops by 0.35, and the improved BWP (IBWP) value only drops by 0.288, which shows the smallest drop among all the algorithms. When the number of nodes is 4, the running time of the optimized KMC algorithm for processing the COVTYPE data set is 1922 s after optimization, and the running time for processing the IRIS data set is the shortest (113 s). When the number of parallel nodes is 10, the speedup ratio of the optimized KMC algorithm for processing COVTYPE data set is 4.16, and the maximal expansion rate is 0.81. The clustering accuracy of traditional KMC algorithm is 89.33%. After optimization, the clustering accuracy of KMC algorithm is 98.67%. The leader factor, offensive contribution factor, shooting stability factor, and passing ability factor in the core grouping are all at the maximum, which are 0.59, 0.51, 0.47, and 0.43, respectively. The optimized KMC algorithm has been shown to reduce the number of iterations, reduce convergence time, and improve clustering accuracy. The optimized KMC algorithm has been shown to reduce the number of iterations, reduce convergence time, and improve clustering accuracy. The conclusion of this study can provide reference basis for big data clustering and visual management.

Finding your favorite videos from massive sports video data has become a big demand for users, accurate sports videos can better help people learn sports content, and the traditional data management and retrieval methods using text identifiers are difficult to meet the needs of users, so the research on the extraction of sports objects in sports videos is of great significance. This paper mainly studies and proposes the basketball object extraction method based on image segmentation algorithm and can accurately analyze the trajectory of the basketball target. By modeling the video frame of basketball game, the basketball object is selected for segmentation and extraction. The extracted basketball object can be used for tracking the target in the basketball video clip retrieval system. At the same time, the segmentation and extraction of the basketball object are also the core part in the basketball video clip retrieval framework. Combined with the characteristics of basketball video images in the database, the algorithm extracts the image block variance and contrast to form the training feature vector, and the correct segmentation rate on the database is higher than 95.2%. The results show that this method has a good effect on the segmentation and extraction of basketball objects in basketball videos.

If the load exercise exceeds a certain degree, it will lead to sports injury. The main reason for this phenomenon is that the human body produces a lot of free radicals after sports training. Free radicals can attack human cells and cause lipid peroxidation to damage cell membrane. The human body can improve the antioxidant capacity of the body by supplementing some trace elements. Selenium, iodine, zinc, iron, and calcium are all trace elements that contribute to antioxidants in the body. Nanoselenium is of great interest among many immune modulators because of its high antioxidant properties and remarkable immune protective function. In this paper, grey rabbits were used as the research object to carry out aerobic endurance training. Nanoselenium and placebo were supplemented in each group. The evaluation model of nanoselenium on aerobic endurance exercise was established by system control method, exhaustion compensation method, and analytic hierarchy process. The adaptive changes of nanoselenium on aerobic endurance exercise of grey rabbits were studied in detail, and the effects of exercise and antioxidant on the body were observed. Compared with the previous research methods, the difference is that the decentralized control theory is introduced as the guiding ideology of the research. According to the experimental results, the accuracy of the overall experimental results is improved by about 20%, and the accuracy is higher, which has certain practical value.

In order to effectively detect and monitor athletes and record various motion data of targets, the study suggests a study of target tracking algorithms to detect the direction of motion video sports movement based on the neural network. A class of feedforward neural networks with convolutional computation and deep structure is one of the representative algorithms of deep learning. Firstly, the athlete image is obtained from the video frame; combined with the nonathlete image to construct the training set, use the bootstrapping algorithm to train the convolutional neural network classifier. In the case of input picture frames, pyramids of different scales are then constructed by subsampling, and the location of many candidate athletes is detected by a neural network of disruption. Finally, these centers calculate the center of gravity of the athletes, find the athlete to represent the candidate, and determine the location of the final athlete through a local search process. The results of the experiment show that the proposed scheme of 6000 frames in the two game videos is compared with the AdaBoost scheme, and the detection rate of the proposed scheme is 75.41% to calculate the average detection accuracy and false alarm speed of all players. The detection rate is higher than the AdaBoost scheme. Therefore, this scheme has a high detection rate and low false positives.