Academic emotions refer to various emotional experiences in connection with learners’ academic activities in the learning process, which are vital to the development of learners’ physiology and mentality. Facial expression recognition (FER) technology has been widely used in online learning to recognize learners’ academic emotions. However, learners often inadvertently cover part of their face with their hands in online learning, which affects the recognition accuracy on academic emotions. Most existing databases lack facial expression data with hand occlusion, which makes it difficult for researchers to further improve recognition accuracy. Therefore, this research established an online learners’ facial expression database with hand occlusion (OLFED-HO) to solve the above problem. This database has a total of 92947 facial expression images of online learners, including four different hand occlusion situations (no occlusion, left occlusion, middle occlusion, and right occlusion) and seven academic emotions (confusion, curiosity, distraction, enjoyment, fatigue, depression, and neutral). Then, in order to prove the high reliability of our database established in this study, we analyzed the confusion matrix and concluded that the expression labels marked by different external coders have a high internal consistency. Such a database is expected to further promote the application of expression recognition technology in the field of education, and fill the gap of the academic emotion database with hand occlusion. In addition, an automatic facial expression recognition method with transfer learning based on region attention networks (RAN) is proposed in this paper, which efficiently reduces the impact of hand occlusion. And the proposed architecture achieved an accuracy of 89% on the test set of our database.