Due to the rapid developments in network technology, the efficiency of image data acquisition, transmission, and storage has been significantly enhanced, leading to an explosive growth in the volume of image data on the internet. Deep hashing models have swiftly emerged as a prominent solution in the field of image retrieval due to their exceptional retrieval accuracy and efficiency. The influx of new data can result in changes in the data distribution, resulting in the problem of concept drift. Furthermore, the new data comprise a significant amount of unlabeled data. To address these issues, we propose a semi-supervised incremental deep hashing image retrieval model. (1) To address the issue of concept drift, we propose a method of using predefined cluster centers for clustering. This method employs the Hadamard matrix and Bernoulli distribution to generate cluster centers in advance, effectively ensuring a reasonable distribution of clustering centers of both original and incremental data. (2) To address the issue of unlabeled data, we propose a pseudo-label discriminator based on similarity probability to generate pseudo-labels for unlabeled data, which effectively improves the effect of semi-supervised training of the model. Extensive experimental results demonstrate the effectiveness of our proposed method, which can effectively address the concept drift issue under semi-supervised conditions.