At present, college students give great importance to their physical training, but their physical habits are poor and they cannot exercise regularly. In view of the influencing factors in the habit formation of physical exercise, this paper puts forward a continuous discrete algorithm to provide students with a scientific and reasonable habit formation scheme. First, the data of college students’ physical exercise habits are collected, and the data are cleaned and continuously analyzed to initially form a habit formation data set; Then, the information gain and clustering thought in the discrete algorithm are used to judge the data change, and the ordered relationship among the factors is obtained. Finally, in judging the influence degree of each factor, we find out the final influencing factors. MATLAB simulation shows that the continuous discrete algorithm can accurately analyze the problem of physical exercise habit formation and sort the influencing factors, with an accuracy rate of 90% and a calculation time of less than 19 minutes, which is significantly better than the original discrete algorithm.

As the national sport of our country, table tennis focuses on continuous innovation and development. Table tennis is a high-quality sport characterized by fast movement and great flexibility. Unlike other human behaviors, identifying ping pong shots is idiosyncratic and difficult. Table tennis training is extremely important, and correct training can make athletes progress. In this paper, based on the perception of the Internet of Things, the action recognition and application of table tennis training actions are carried out, and the following conclusions are drawn: (1) the training movements of table tennis are relatively complex, which has a great challenge to the action recognition technology. The action recognition technology based on IoT perception can efficiently identify table tennis training actions. (2) Analyze and study the action recognition algorithm based on IoT perception, and propose a higher recognition accuracy and more stable algorithm DTW. (3) Comparing the accuracy, loss rate, and time impact between the algorithm in this paper and the traditional recognition algorithm, it is concluded that the algorithm proposed in this paper has higher accuracy and lower loss rate than the traditional algorithm. And with good stability, it is not affected by the environment and time. The algorithm in this paper is an algorithm with better performance and more worthy of use.