Integrating the Bigdata and Deep Learning Analysis Human Movement to Improve the Sports

Key technological tools driving the fast growth of integrated manufacturing may be found in information technologies including big data, deep learning, cloud computing, and the Iot. Recent years have seen significant progress in the application of deep learning to the analysis of large datasets. This work focuses on the automated comprehension of human motions in free gymnastics movies as part of the research of a high-precision classification model for sports footage. This work uses deep learning and large-data-based computer imagery to identify and represent human motions in videos. Sports footage is classified using an independent narrative based on long- and short-term memory associations. Short-term and long-term memory networks learn the map relationship between word sequences and video frame sequences in S2VT. We recommend stressing freestyle gymnastics' building blocks. This study creates a competitive freestyle gymnastics routine database. Using predicted sampling and data and custom-built dataset evaluation, we can narrow training decoder-prediction decoder disparities. This research found rethinking sports footage categorization better. Deep learning video categorization with big data enhances user experience by classifying videos more correctly. Feature extraction networks automate free gymnastics movement descriptions and compare feature extraction results to classify lifting sports models.