Emotional Factor Recognition and Teaching Strategy Adjustment in Cultural Teaching Supported by Artificial Intelligence

Emotional factors are key variables that affect the effectiveness of cultural teaching, not only affecting students’ learning interest, but also closely related to learning efficiency and the degree of knowledge internalization. With the rapid development of artificial intelligence technology, the application of emotion recognition technology provides a new solution for optimizing cultural teaching. Emotion recognition technology collects students’ facial expressions, speech features, and physiological data, and uses deep learning algorithms to analyze students’ emotional states in real time, providing accurate feedback support for teaching. This article takes emotional factors in cultural teaching as the starting point and systematically explores the application value and practical path of emotion recognition technology supported by artificial intelligence in cultural teaching. By sorting out the key links of emotion recognition technology and analyzing its specific applications in dynamic adjustment of teaching strategies, such as personalized resource recommendation, teaching mode design, and emotional intervention methods, suggestions for optimizing intelligent teaching strategies are proposed. Research suggests that the introduction of emotion recognition technology can not only effectively enhance students’ learning participation and effectiveness, but also promote personalized and dynamic adjustment of teaching content, thereby significantly improving the overall efficiency and effectiveness of cultural teaching. In the future, it is necessary to further improve the deep integration mechanism of artificial intelligence technology and cultural teaching on the basis of ensuring data privacy and technological ethics, and promote the intelligent and precise development of cultural teaching.