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.

In today’s world, data visualization is employed in every aspect of life, and online course makers should take use of the wealth of behavioral data provided by students. Currently, data visualization is being used to suit the development needs of online education in the Internet age. It is also a strong assurance for the online course platform’s improvement and implementation. Data visualization is already closely related to our lives. For online education, the application of data visualization can help course builders understand learners’ learning time characteristics, learning behavior habits, and learning improvement effects, so as to provide learners with corresponding learning guidance, solve learners’ learning difficulties, and improve learning efficiency and course teaching quality. In order to confirm the improvement effect of visualization technology on online learning, the following work is done in this study. This study describes the current state of visualization technology in the United States and internationally, as well as the foundation for the prediction approach that will be proposed later. There are many factors in the evaluation of the online learning effect, and it is dynamic, which is a nonlinear manifestation. The nonlinear computing, self-learning, and high fault endurance of artificial neural network technology are used in this article, and an online learning effect improvement prediction model based on the improved BP neural network is established, namely, the Levenberg–Marquardt back propagation (LMBP) prediction model. The experimental results suggest that the model has a good level of accuracy and may be used to forecast the effect of online learning improvement.