Predictive Analysis of Class Attention Based on CNN Model

Abstract

Abstract As the current classroom teaching reform has been integrated into every class of higher vocational colleges, modern artificial intelligence, deep learning, big data and other high-end technologies are integrated into the classroom, and classroom teaching has become more and more diverse. But the attention of modern vocational and technical students to the classroom has also received widespread attention from the society. This article uses artificial intelligence and big data technology to analyze class attention, and then uses the CNN model to predict the overall class attention. This article discusses the core literacy strategies for cultivating the efficiency of vocational and technical students in the classroom, and proposes methods such as setting suspense to guide independent inquiry, organizing learning to discover learning rules, and in-depth exploration of students’ independent learning ability, so as to increase the attention of the classroom. Based on the current popular CNN target detection algorithms can be divided into two categories. The first category is a two-stage detection algorithm, which divides the detection problem into two stages. First, generate candidate regions, and then classify the candidate regions. The typical representative of these algorithms is the R-CNN series algorithm based on region proposal. The other type is the 1-stage detection algorithm, which makes predictions for every part of the image, that is, it does not need to generate candidate regions. On this basis, a target prediction system based on the CNN model is designed and implemented, which predicts and analyzes the class attention of higher vocational colleges, uses a deep learning framework, and uses PyQt5 as an interface development framework combined with python. Experimental research results show that the designed CNN-based classroom attention model in higher vocational colleges can effectively predict and analyze students, and the effect of the practical experiment is good.