Although the development of MOOCs has revolutionized the method in which we study, it also presents substantial hurdles in terms of ensuring that learners are engaged and motivated. On the basis of head posture characteristics, we propose an Intelligent Learning Behavior Analysis and Intervention System for English MOOCs in order to address this issue. Through the utilization of computer vision and machine learning techniques, our system is able to assess the aspects of learners’ head posture, determine the degrees of engagement they are experiencing, and deliver individualized interventions to improve the results of their learning. The Head Posture Analysis Module uses computer vision to extract head posture parameters from learner videos. The involvement Detection Module uses machine learning to assess student involvement based on head posture. Customized treatments including adaptive difficulty adjustment, interactive components, and social learning are offered in the Intervention Module to boost student engagement and motivation. To begin, it offers a fresh method for studying the behavior of learners and determining the extent to which they are engaged in the learning process. Second, it provides individualized interventions that are designed to enhance the learners’ motivation and the results of their learning. Lastly, it has the ability to increase the overall quality of MOOCs as well as the experiences that learners have.