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With the vigorous development of China's sports industry, the rules and number of events are increasing, and the competition pressure on the playground is also increasing. The increase of competition pressure will bring many negative effects to athletes. In order to relieve the pressure of athletes in sports competition and eliminate the negative significance of pressure to athletes, this paper mainly introduces the clustering algorithm of sports source and competition. The clustering algorithm uses the similarity of attributes between data objects to calculate the clustering structure of fractional clustering. In this paper, the original data of sports competition pressure are obtained through the questionnaire survey, using clustering and recommendation algorithms to calculate and analyze the original data, the data utilization rate is as high as 98%, and the analysis efficiency is as high as 97%. Dividing athletes into three categories, the magnitude and source of stress are analyzed, respectively, and application methods are recommended according to their respective stress distributions, so as to assist psychologists in the diagnosis, and the corresponding height is 80%; this enables athletes to receive good counseling advice and remain mentally healthy. [ABSTRACT FROM AUTHOR]

As China becomes more and more international, the number of people traveling abroad is also increasing. The demand for English recognition is becoming more and more vigorous, and traditional translation software is time-consuming, laborious, and less accurate. This article optimizes the target detection model YOLOV3. Firstly, the image is divided into multiple model structures, and the K-means++ clustering algorithm is used to determine the target detection prior frame value and the high frame of the corresponding frame according to the characteristics of the English image. Then, by using K-means++ clustering algorithm to optimize the anchor parameters, the model structure is better adapted to the English identification dataset scene; finally, the feature information extracted by the DarkNet-53 model is spliced to improve the structure of the YOLOV3 convolutional layer, using 3090 graphics card GPU to perform multiscale training and testing. Experimental results show that the improved YOLOV3 algorithm in this paper has a mAP of 0.95 on the English identification dataset and a detection speed of 50fps, which is 0.11 higher than the mAP before optimization. Therefore, optimizing the YOLOV3 algorithm in this article has a good effect. In the future, English translation will become a necessary software program for Chinese people to go abroad. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has strongly affected education in China, even if education departments and corresponding schools took a series of measures to manage online education of the school's new semester in China, including maneuver, learning platform allocation, and teacher training. In this paper, edge computing is used to optimize online education, and a task offloading algorithm is designed to minimize the computing delay of terminal tasks. Through preparation, practice, and reflection of this online education, this study aims to comprehensively demonstrate the learning condition of online education in China and present the real adjustment impact based on the problems encountered during the process. Although several schools gradually reopened to students in 3 months, several improvements are warranted in various ways. This study proposes the construction of education infrastructure, the adjustment of teaching organization, and the learning methods of teachers and students, providing a clear guiding significance for the development and enhancement of online education in the future. [ABSTRACT FROM AUTHOR]