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Top1. Introduction
As we all know, the 21st century is an information age. With the development of human social productivity and the continuous change of social production structure, information is increasingly becoming an important means to optimize production structure and coordinate production mode, as well as an important way to promote the reform of supply side structure and coordinate production relations (Vu et al. 2019). As a common form of the combination of human and computer, information processing system has been widely used in all aspects of scientific research, social production and life. It has become an important tool for all walks of life to obtain, store, process, communicate and use information (Taban et al. 2017).
Now, with the rapid popularization of smart phones and wearable smart devices, because their devices contain sensors such as three-axis gyroscope, accelerometer and optical heart rate sensor, it is no longer difficult to obtain a large amount of human motion data such as people's heart rate, geographical location and sports, and a series of running fitness applications have been born, It has led the running wave of the whole people and played a great role in promoting the rapid development of the sports industry (Wallis et al. 2020).
However, most wearable devices on the market today still remain in the original basic functions such as step recording and movement speed measurement (Zhang et al. 2020). They do not have the function of movement behavior recognition, let alone scoring and evaluation, personalized movement guidance and other functional services on this basis. Many sports wearable smart device manufacturers only collect a large number of users' physical and sports data through their own products and store these data on their own cloud platform, but lack of in-depth mining of these data, which not only makes a serious waste of a large number of data resources of the enterprise, but also reduces the operation efficiency of the enterprise, and cannot bring convenience to users with impressive service experience (Songan et al. 2019). Some studies (Vulchanova et al. 2019, Burggrf et al. 2021, Karduni et al. 2019) have shown that the interaction between people and information systems and cognitive science have a positive impact on improving the efficiency of the system. Therefore, if we can use data mining and artificial intelligence related technologies while establishing the information processing system, we will effectively improve the operation performance of the whole system and improve the user's operation experience.
At present, in the field of human motion recognition information processing, with the rise of deep learning, deep learning algorithms are also used in the field of sensor-based motion pattern recognition, which overcomes the defect that the traditional recognition and classification algorithms need to manually select relevant motion information features (Pan et al. 2020). It has brought a great breakthrough in this field and greatly accelerated the process of intelligent pattern recognition (Naser et al. 2020). In particular, with its special structure design, the cyclic neural network has strong processing ability for the data related to the front and back input, and has been widely used in the field of time series prediction (Connor et al. 2020). In view of its efficient and intelligent processing ability in processing time series data, this paper explores the use of cyclic neural network to identify and process the data collected by wearable devices, through massive data training, a model that can accurately identify human movement behavior is formed, and on this basis, a human movement data acquisition and feature analysis system based on sports big data is designed and developed.