Visual Management of Sports Based on Intelligent Analysis of Big Data

Abstract

After years of development, big data technology has become more mature and reliable in its application in various fields. With the explosive growth of data and the rise of data complexity, the processing and application of data have actually changed our daily life. The development of data in sports is very active in recent years, and the natural dependence of sports on data determines that the visualization of sports has a great creative space in data sports. Therefore, it is necessary to deeply study the visual management of sports under the background of big data technology. As we all know, in sports, competitive games occupy the vast majority, and the data generated by this is not limited to the score of the game. People are paying more and more attention to information related to sports, but these information are generally very complicated and huge. This is also an area with the widest audience, and the core of competitive sports is data. The final results and progress of most sports competitions are reflected through changes and comparisons of data. With the continuous development of big data information collection technology, the available sports data are more detailed, which also makes sports develop toward visualization. Therefore, with the help of the excellent ability of big data in screening data, this article proposes algorithms such as DTW and recurrent neural networks to reasonably and reliably analyze and process a large number of data generated in the process of sports and embeds an error analysis module in the designed model to ensure the accuracy requirements in data processing to a greater extent. The model designed in this article has been able to improve the accuracy by more than 86%. This will greatly facilitate our management and have a far-reaching impact on sports visualization.