Design of Classroom Intelligent Assistance System under Wireless Network and Deep Learning

Abstract

With the development of science and technology, a variety of computer technologies have emerged. The disadvantages of traditional education appear one after another. It is difficult for teachers and students to interact synchronously, and classroom efficiency is not high. Therefore, it is urgent to improve classroom efficiency and the interaction between teachers and students. In this paper, the hardware and software modules are analyzed and studied using wireless network technology, and deep learning convolutional neural network architecture is constructed. The neural network is trained until the optimal model is obtained. The system consists of many scientific front-end technologies. It is a system structure that integrates various information technologies such as computer vision, network communication, wireless sensors, analog electronic technology, and digital electronic technology. The results show that the model generates the most network structures irregularly when the loss rate of hidden nodes is 0.5. Besides, the training effect of the model is the best. In addition, the recognition accuracy of the training dataset can reach 0.98 after the model iteration round is ten. The recognition accuracy of the validation dataset is 0.6. After model iteration, the recognition accuracy can be improved by 8.6%. The performance of the system can be further optimized. In addition, the intelligent assistance system has completed multiple data iterative updates, and the performance of the system can be optimal. The system can ensure the quality of teachers’ teaching, improve the quality of students’ classroom learning, and adjust the classroom atmosphere. This paper has important reference value for enhancing the interaction between teachers and students and improving learning efficiency.