Oracle Bone Inscriptions (OBI), as the oldest form of Chinese writing, bears the precious heritage of ancient Chinese civilization. Deciphering OBI has been a monumental challenge in archaeology and philology. However, the preservation method of OBI through rubbings faces significant challenges due to severe noise interference, adversely affecting the accuracy of OBI detection. Additionally, existing traditional object detection algorithms inadequately consider the unique characteristics of OBI, making it difficult to effectively identify Oracle characters on OBI rubbings. To address these challenges, this paper proposes a novel approach to OBI detection, Oracle Character Prototype-guided cyclic disentanglement for OBI detection. These features are amalgamated into distinct character structure prototypes by capturing shared character structure features among different Oracle characters. This approach highlights the distinctive forms of Oracle characters, enhancing feature clarity and recognition. The Oracle Character Prototypes enable the model to learn common features across different character forms, improving its adaptability to various Oracle character structures and enhancing its capability to handle complexity. Moreover, to mitigate the impact of noise on detection, we introduce a cyclic disentanglement method for Oracle Bone Inscriptions. This process, utilizing the Oracle Character Prototypes and contrastive loss as supervisory signals, cyclically decouples Oracle features, isolating information rich in character structure features. This effectively mitigates the influence of noise on OBI detection. Comprehensive evaluations were conducted, comparing our method with state-of-the-art object detection algorithms, validating its effectiveness both qualitatively and quantitatively.