The challenge in fine-grained image classification tasks lies in distinguishing subtle differences among fine-grained images. Existing image classification methods often only explore information in isolated regions without considering the relationships among these parts, resulting in incomplete information and a tendency to focus on individual parts. Posture information is hidden among these parts, so it plays a crucial role in differentiating among similar categories. Therefore, we propose a posture-guided part learning framework capable of extracting hidden posture information among regions. In this framework, the dual-branch feature enhancement module (DBFEM) highlights discriminative information related to fine-grained objects by extracting attention information between the feature space and channels. The part selection module selects multiple discriminative parts based on the attention information from DBFEM. Building upon this, the posture feature fusion module extracts semantic features from discriminative parts and constructs posture features among different parts based on these semantic features. Finally, by fusing part semantic features with posture features, a comprehensive representation of fine-grained object features is obtained, aiding in differentiating among similar categories. Extensive evaluations on three benchmark datasets demonstrate the competitiveness of the proposed framework compared with state-of-the-art methods.