This paper aims to study the application of content recommendation based on big data in distance education course design. With the rise and development of distance education, students are faced with problems such as information overload and insufficient learning resources in the process of receiving education. The traditional education model cannot meet the individual learning needs of students. Therefore, this study adopts a data-driven approach and uses big data analysis technology to achieve an accurate grasp of students’ interests and learning preferences. At the same time, the mixed learning intervention model is used. First, through the collection and analysis of large-scale learning data, the individual portrait of students is built. Using data-driven content recommendation algorithm, combining students’ personal portraits and course characteristics, we recommend the most suitable learning content for students by matching degree and other indicators. Experimental verification shows that compared with traditional methods, the content recommendation model based on big data can more accurately understand the needs of students and give accurate and personalized recommendations in the course design of distance education. According to the experimental results, the application of the learning intervention model successfully improved the learning effect of students, and the academic performance increased by 8.5% on average. Content recommendation based on big data shows an important application prospect in distance education course design. In the future, the method is expected to play a greater role in the field of distance education to provide students with a personalized and efficient learning experience.