Enhancing College English Learning through Digital Art-Infused Smart Classroom Models Construction Industry with Fuzzy Neural Networks

Abstract

Traditional teaching practice activities have kept pace with modern science and technology, but the Internet and modern information technology are increasingly used in teaching practice activities. As a result of the development results of the combination of modern network technology and teaching practice activities, the hybrid teaching mode has emerged as one of the most common teaching modes. Teaching practice activities benefit from activities because they provide a more comprehensive personalized space and a broader range of resource utilization options for the students participating in them. Fuzzy rough neural networks are used in this paper to address the low recommendation efficiency of the English resource recommendation module in a smart classroom and the insufficient use of related information. First, a personalized recommendation model is proposed, and the recommendation algorithm flow is given in accordance with the association rules in this method The above model is solved using a fuzzy rough neural network, and the characteristics of user behavior are obtained by calculating the fuzzy correlation coefficient and fuzzy correlation degree. This is all happening simultaneously. Prediction error can be reduced to some extent by using this algorithm in comparison to other algorithms. This means that students will receive more accurate recommendations.