Aiming at the problems of poor average fitness, low-risk prediction accuracy, high mean square error, low-risk evaluation precision, and long average running time of traditional sports event model evaluation and prediction methods, a sports event model evaluation and prediction method using principal component analysis (PCA) is proposed. Sports event risk monitoring microbase is deployed by ZigBee technology, and sports event risk monitoring data is monitored and packaged at each base station. Optical fiber and Ethernet are used to transmit the data to the monitoring and management center to complete the risk data collection of sports events. After data standardization, the risk evaluation index system of sports events is constructed, and the comprehensive score of each risk index of sports events is obtained by using the PCA method. The BP neural network is improved by genetic algorithm (GA), and the comprehensive score of risk index is input into the network to obtain the evaluation and prediction results of sports event risk. The results show that the proposed method has good average fitness, the predicted value of sports event risk is almost equal to the actual value, the prediction mean square error is less than 0.15, the evaluation precision is high, and the average running time is only 8 s. The cost (time complexity) is low. Overall, the method has a good application prospect in the field of sports event evaluation and prediction.

There are some problems in the current human motion target gesture recognition algorithms, such as classification accuracy, overlap ratio, low recognition accuracy and recall, and long recognition time. A gesture recognition algorithm of human motion based on deep neural network was proposed. First, Kinect interface equipment was used to collect the coordinate information of human skeleton joints, extract the characteristics of motion gesture nodes, and construct the whole structure of key node network by using deep neural network. Second, the local recognition region was introduced to generate high-dimensional feature map, and the sampling kernel function was defined. The minimum space-time domain of node structure map was located by sampling in the space-time domain. Finally, the deep neural network classifier was constructed to integrate and classify the human motion target gesture data features to realize the recognition of human motion target. The results show that the proposed algorithm has high classification accuracy and overlap ratio of human motion target gesture, the recognition accuracy is as high as 93%, the recall rate is as high as 88%, and the recognition time is 17.8 s, which can effectively improve the human motion target attitude recognition effect.

To address the problems of the traditional human motion gesture tracking and recognition methods, such as poor tracking effect, low recognition accuracy, high frame loss rate, and long-time cost, a dynamic human motion gesture tracking and recognition algorithm using multimode deep learning was proposed. Firstly, the collected human motion images are repaired in the three-dimensional (3D) environment, and the multimodal 3D human motion model is reconstructed using the processed images. Secondly, according to the results of model reconstruction, the camera gesture and other parameters of the keyframe are used to construct the target tracking optimization function so as to achieve the purpose of accurate tracking of human motion. Finally, for multimodal human motion gesture learning, a convolutional neural network (CNN) is developed. The trained CNN is utilized to complete dynamic human motion recognition after convolutional and pooling calculations. The results demonstrate that the proposed algorithm is effective in tracking human motion gestures. The average recognition accuracy is 96%, the average frame loss rate is 8.8%, the time cost is low, and the proposed algorithm has a high F-measure and much lower power consumption than other algorithms, indicating that the proposed algorithm is effective.

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