As people have become more aware in recent years, aerobic physical exercise plays an important role in alleviating people’s mental health problems. However, traditionally, it is believed that young children do not have mental health problems. To help people change this fixed idea, and to study how to correctly adjust the load intensity of aerobic physical exercise under the condition of limited physical fitness of young children, and accurately help children’s mental health development, this paper studies the influence of aerobic physical exercise load intensity on children’s mental health. In this paper, the detection and tracking technology of video moving objects is used to analyze the data of the research object. This technique includes several commonly used and improved video analysis algorithms. The use of moving target and tracking technology and algorithms can completely extract moving targets, eliminate the phenomenon of void and nothingness, and improve data acquisition and analysis capabilities. The results show that taking part in aerobic exercise with appropriate intensity is beneficial to regulating children’s emotional state, reducing their psychological burden, enhancing their negative energy resistance, and arousing their positive participation. Compared with before aerobic exercise, the learning efficiency was improved by 6.36%.

Along with the main feedback information of early childhood education activities, children’s movement evaluation plays an important guiding role in early childhood education activities. Teachers can teach children in accordance with their aptitude according to the results of children’s sports evaluation. Traditional physical education overemphasizes the leading role of teachers, and young children can only passively receive the education of teachers. Teachers always use the same standard to evaluate each child, simply assigning a rating of “strong,” “moderate,” and “poor” to children, ignoring the differences in children’s physical intelligence. The emergence of multidata fusion technology can use the differences and complementarity of various data in evaluation functions to make up for the insufficiency of a single exercise evaluation result, purposefully choose the evaluation method flexibly according to the content of the exercise and the scene of the exercise, and gradually improve the level of children’s exercise evaluation. This paper studied the children’s motor intelligence evaluation system based on multidata fusion. To this end, this paper will focus on children’s autonomous sports games and determine the first-level indicators in the children’s sports evaluation index system as four indicators: classroom performance, physical fitness, motor skills, and extracurricular fitness. It used the principle of multidata fusion, firstly evaluates children’s exercise physiology data through a fuzzy neural network algorithm, and then combines the adaptive weighted data fusion algorithm with D-S evidence theory to evaluate children’s movement intelligence. Experiments showed that the multidata evaluation system can take effective measures to intelligently evaluate children’s comprehensive motor ability. Compared with the traditional evaluation method, the evaluation results are increased by 5%, and the children’s sports evaluation results are more average, which can enhance children’s sports confidence and promote children’s effective exercise.