Application of Computer Big Data Technology in the Evaluation System of Track and Field Auxiliary Training

Using a hybrid gene neural network, this paper introduces the special data of track and field athletes. According to the adjustment between the internal nodes of the system, the special data information of athletes is processed, and the grey prediction model of technical level is constructed. In this paper, the sprinting technique in track and field in China is studied by using neural network technique. The statistical theory is used to predict the time series of sprint performance in different periods. Secondly, the phase space reconstruction of the motion performance in different periods is carried out based on the phase space theory. Finally, a dynamic analytical model of the evolution trend of motion performance is constructed based on neural network and interval phase space theory. In this way, the dynamic analysis of the motion state is realized. In the process of data processing, the advantages of the structure of the data are used to parallel the data that cannot be associated in the original data. The simulation results show that the algorithm provides accurate reference data and usage attributes for the prediction process.