In the contemporary field of unsupervised deep hashing research, methods predicated on contrastive learning are predominant. However, sampling bias brought about by the random extraction of negative samples in contrastive learning deteriorates image retrieval accuracy. To address the issue, this study proposes a novel unsupervised deep hashing based on bias suppressing contrastive learning (BSCDH). It proposes a bias suppression method (BSS) based on a contrastive learning framework. This method approximates incorrect negative samples as extremely hard negative samples and designs a bias suppression coefficient to suppress these extremely hard negative samples, thereby alleviating the negative impact of sampling bias. The corresponding suppression coefficient value is determined based on the similarity between the current negative sample and the query sample. Distance relationship between the current negative sample and adjacent hash centers is introduced to correct the suppression coefficient value, reducing the possibility of excessive suppression of normal negative samples. Ultimately, the mAP@5000 of the BSCDH method (64 bits) achieves 0.696, 0.833, and 0.819 respectively on the CIFAR-10, FLICKR25K, and NUS-WIDE datasets, demonstrating a significant performance advantage over the baseline. Extensive experiments conducted in this paper verify that BSCDH exhibits high retrieval accuracy in unsupervised image retrieval methods and can effectively address sampling bias.