Abstract: Learners often have problems such as “information overload” and “information trek” when facing the vast online learning course resources. Based on learners’ learning records, it is increasingly important to recommend MOOC courses to learners that are consistent with their knowledge preferences and learning needs. Aiming at the problems that the existing MOOC recommendation methods do not make full use of the implicit information contained in MOOC videos, which are easy to form a “cocoon effect”, and it is difficult to capture the dynamic learning needs and interests of learners, a dynamic MOOC recommendation model integrating video subtitle information MOOCDR-VSI is proposed, which uses BERT as the encoder to deeply mine the semantic information of MOOC video subtitle text by integrating the multi-head attention mechanism. The network based on LSTM architecture is used to dynamically capture the changing knowledge preference state of learners with learning, introduce the attention mechanism to mine the personality information and common information between MOOC videos, and finally recommend MOOC videos with Top N recall probability combined with the knowledge preference status of learners. The performance of MOOCDR-VSI is analyzed by MOOCCube in the experimental dataset collected in the real learning scenario, and the results show that the proposed model improves the HR@5, HR@10, NDCG@5, NDCG@10 , NDCG@20 evaluation indexes by 2.35%, 2.79%, 0.69%, 2.2% and 3.32%, respectively, compared with the current most optimal method.