Since online hashing has the advantages of low storage and fast calculation ,it attracts the attention of many scholars. However, the learning of new data streams separates the similarity between new data and existing data in many online hashing methods, which leads to poor retrieval performance. In addition, the similarity measure ignores the expression of different similarity. In this paper, we propose a novel supervised method, namely Label Projection Online Hashing for Balanced Similarity (LPOH). Compared with existing online hashing methods, LPOH aims to solve the problem of the effective establishment of the projection between the label vector and the binary code, and the successful realization of description of different similarity between the same labeled data. Specifically, LPOH overcomes the problem of similarity deviation caused by data imbalance via establishing a mapping matrix to derive a relationship between the data label vector and the binary code. Furthermore, the error between the binary code and the hash function concerning data streams is described. Extensive experiments on widely-used three benchmark datasets demonstrate that LPOH outperforms the state-of-the-art online hashing methods.