Batch-based hash learning methods are usually inadequate for real-time online retrieval of large-scale streaming data. Therefore， online hashing has emerged as a promising solution， enabling the learning of hash codes for new data without revisiting old data and adapting hash functions to coming data. However， several challenges persist， including semantic drift caused by insufficient exploration of inter-class relationships and data forgetting resulting from limited association between new and old data. To address these challenges， this paper proposes a novel supervised method named online semantic enhancement hashing （OSEH）. It designs a triple matrix factorization framework， which mutually bridges the gap of original features and one-hot labels， thereafter constructing a fine-grained label matrix. Moreover， by seamlessly integrating label embedding and pairwise similarity， the proposed method effectively embeds enriched semantics into the process of hash learning， optimizing both hash code and function. Experimental evaluations conducted on benchmark datasets validate the effectiveness of the proposed method.