Bodybuilding operation is a favorite sports item, which is beneficial to enhance physical fitness, improve coordination and flexibility of movement, and enhance cardiopulmonary function. Standard aerobics movements and nutrition matching strategies can more effectively enhance the exercise effect. In this paper, the characteristics of aerobics, sports, and nutrition strategies are researched through the human body recognition technology of the depth image; the subjects were divided into four groups by statistical method and control variable experiment method. They were the control group, the experimental group for aerobics training, the experimental group for improving the nutritional balance under diet, and the experimental group for aerobics training. After two and a half months of experimental training and observation, the students in the four groups were tested for physical fitness and physical function; analyze the obtained experimental test data using the human body recognition technology of depth image, then obtain the experimental test data, and then use the data fusion method to combine the data and information for more accurate evaluation. The results showed that after 10 weeks of aerobics learning and nutrition, the average height of students increased by 1.19 cm, the average weight decreased by 1.17 kg, the number of sit-ups increased from 23.9 before the experiment to 31.2, and the results of 50-meter race and 800-meter run were 0.2 seconds and 2.9 seconds, respectively. It can be concluded that aerobics and nutrition can speed up metabolism, promote the growth and development of bones, and supplement the nutrition needed by the human body, to improve students’ physical quality. This study contributes to the research of sports and nutrition matching in improving physical conditions.

Multiagent technology, as a conceptual model commonly used in intelligent systems, has now had a lot of related research, and it is also widely used in sports training simulation systems. This paper mainly studies the development of the sports training simulation system based on agent technology. This paper proposes the motion perception system of multiagent technology in sports training. In the research, this paper introduces the related concepts and methods of agent in detail. In order to better simulate the changes of human joints during exercise with the system, this article uses a variety of comparison methods. Among them, the experiment of the imaging analysis module is carried out through the simulation of the simulation system. The experimental results show that the role of multiagent in the simulation system is huge. In linear motion, the accuracy of joint extraction has reached more than 95%. The extraction of joints in the curve movement reached more than 80%, indicating that it can not only complete the relevant processing adaptively but also predict and correct the exercise process of the trainer. Therefore, in the research of sports training simulation system, more attention should be paid to the research of curve motion.

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In the traditional teaching mode, teachers have limited time and energy, but the emergence of AI technology-assisted teaching has greatly facilitated students and teachers. They can study and teach anytime and anywhere and can also solve the problem of lack of professional teachers or venues. With the continuous popularization of “Internet + education” technology, the teaching mode has changed, and online courses are gradually accepted by students regardless of time and geographical constraints. This article aims to study the optimization and application of the intelligent scheduling algorithm in the physical education management system based on blockchain technology. This article proposes a combination of Edge Computing technology and intelligent algorithm software and hardware to improve the current difficulties of domestic colleges and universities. The core content of integration, system functions, course scheduling algorithm, database, and other core contents are preliminary designed, hoping to provide ideas for the specific implementation of the college course scheduling system. The experimental results in this paper show that with the support of Edge Computing in solving the CSP problem, the efficiency of various algorithms is relatively high and comparable when the filling rate is less than 90%. However, when the filling rate exceeds 90%, the execution time of the IFS CBS algorithm is relatively longer, but it is generally acceptable. The algorithm and optimization strategy are also implemented, and the performance improvement of the algorithm is compared and analyzed through experiments, which proves the feasibility of the optimization strategy.