Abstract
Physical education is a highly skilled education offered in colleges and universities. Teachers do not appear in front of inanimate machines as laborers, and they are not the same as gardeners who grow colorful trees, according to their essential characteristics. Their work is aimed at flesh-and-blood students who are sentimental, thoughtful, and engaged in critical thinking. As a result, schools should prioritize physical education and place a premium on emotional infiltration education, improve learning interest, improve teacher-student relationships, create a harmonious teaching environment, and improve teaching quality; it has a significant impact on an individual’s entire life. The modern educational process places a premium on the transmission of rational knowledge while overlooking the accumulation of emotional experience. The cultivation and development of emotional feeling ability, emotional expression, and expression ability receive less attention than the training and improvement of language, concept, logic, and reasoning abilities. Emotion feature clustering is used to propose an emotion recognition method in this study. This method generates extended features for classification by constructing a co-occurrence matrix based on the co-occurrence relationship of emotion features and then by applying the spectral clustering method. The binary value of whether emotional features of emotional education in college physical education appear in a particular cluster is then expressed as a feature and extended to the original training feature set, alleviating the problem of sparse features.
Highlights
In contemporary universities, there will be such a situation, that is, students who are active in the playground will be stronger than those who lack physical exercise, which has to cause concern in schools
Physical education in colleges and universities mainly focused on competitive minority sports, but our requirements should develop into popular sports of art and ecological sports so that sports can become a necessity for people to survive. erefore, in college physical education, physical education teachers should add emotional education instead of teaching physical education [3], improve interest in learning, harmonize the relationship between teachers and students, form a harmonious teaching atmosphere, and improve teaching quality, which even has an important influence on his whole life. e modern educational process emphasizes
The recognition rate of emotional education in physical education is not better, it proves that the language model features and SVM classification method have higher recognition rates, and the network structure of emotional feature clustering inspires us to use a neural network
Summary
Received 18 December 2021; Revised 2 January 2022; Accepted 3 January 2022; Published 14 February 2022. Physical education is a highly skilled education offered in colleges and universities. Schools should prioritize physical education and place a premium on emotional infiltration education, improve learning interest, improve teacher-student relationships, create a harmonious teaching environment, and improve teaching quality; it has a significant impact on an individual’s entire life. Is method generates extended features for classification by constructing a co-occurrence matrix based on the co-occurrence relationship of emotion features and by applying the spectral clustering method. E binary value of whether emotional features of emotional education in college physical education appear in a particular cluster is expressed as a feature and extended to the original training feature set, alleviating the problem of sparse features Is method generates extended features for classification by constructing a co-occurrence matrix based on the co-occurrence relationship of emotion features and by applying the spectral clustering method. e binary value of whether emotional features of emotional education in college physical education appear in a particular cluster is expressed as a feature and extended to the original training feature set, alleviating the problem of sparse features
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