With the development of Internet technology and the outbreak of COVID-19 in 2020, more and more students have chosen online education. However, due to the large number of online courses, students are often unable to find suitable courses in time. A personalized intelligent recommendation system is an effective solution to this problem. Considering the obvious sequential characteristics of users for online learning, an online course recommendation model based on enhanced auto-encoders is proposed. First, the auto-encoder is enhanced with the long short-term memory network, so the model can extract the sequential characteristics of data. Then, the Softmax function is used to recommend online courses. Experimental results show that the proposed method has higher recommendation accuracy than the collaborative filtering algorithm and the recommendation model based on traditional auto-encoders.