Main Article Content
With the popularization of the computer network and the development of artificial intelligence technology, the traditional education industry has been reformed. In the past two years, online education has developed rapidly. The combination of the Internet and education enables students to study online at any time, no longer relying on the time and place requirements in traditional education. However, with the rapid development of online education, many problems have gradually emerged. In online education, with a large amount of knowledge and question banks, students are faced with a large number of choices. Therefore, positioning and tracking the knowledge level of students and realizing personalized online education have become the main problems facing the moment. Based on this, this study integrates deep learning and knowledge tracking technology to build a traditional cultural network online education model, aiming at accurately positioning students’ knowledge levels and recommending personalized question banks. The experimental results show that the average AUC of the model proposed in this study is 0.781, and the average accuracy rate is 0.886, which is significantly better than other online education models. Through the combination of deep learning and knowledge tracking technology, the research successfully provides a new and efficient model for personalized learning in the field of online education, which is of great guiding value for promoting further innovation and development of online education. In addition, the research also provides practical solution strategies for related fields, which have obvious practical significance and popularization value.