Driven by artificial intelligence and deep learning technology, this study proposes an intelligent course recommendation system for art and design education. By constructing XMMC, a joint extraction model of knowledge entities and relations based on deep learning, the accurate analysis of course knowledge structure is realized. Key features such as user preference, content semantics and social influence are extracted by combining multi-feature ranking models such as collaborative filtering, topic modeling and course hotness. Finally, based on the deep reinforcement learning algorithm DDPG, a dynamic recommendation strategy is designed to optimize the recommendation effect. The experiments are based on Coursera Course, Caltech-UCSD Birds 200 and Education Recommendation datasets, and the results show that the improved DDPG model achieves 49.11%, 70.05% and 59.23% course coverage on the three datasets, respectively, which is better than the traditional algorithms Time SVD and CDAE with significant improvement. We constructed the art education course category with the number of topics as 5. In the practical application, the recommended list generated by the system is highly consistent with the course heat analysis, in which the course “Introduction to 3D Modeling and Blender” ranks the first with 6729 average playbacks, which verifies that the recommendation strategy can effectively improve the fitness of the pushed content and the current course progress of the students. It verifies that the recommendation strategy can effectively improve the compatibility between the pushed content and the students’ current course progress.