<p>Diabetic retinopathy (DR) is one of the most common ocular complications in diabetic patients. Therefore, early DR screening is crucial for preventing disease deterioration and timely diagnosis. However, the challenge of DR grading arose due to the tiny and unevenly distributed lesions and the complex pathological relationships, which are difficult to capture. This paper proposes the pathological relationship feature perception and dual attention guided network (PRANet) for DR grading. Firstly, a pathological relationship feature perception module (PRFM) is introduced to capture the pathological relationships between different types of lesions. A lesion detection network is used to locate the rough regions of the lesions, and K-Means clustering is performed on the lesion features to generate lesion nodes. The co-occurrence relationships between lesion nodes are used to construct an adjacency matrix, which is then input into a graph convolutional network to explore the complex relationships among the lesion nodes. Moreover, the EfficientNetV2-M backbone is employed to obtain global information of DR images and input them into a dual attention module (DAM). The DAM utilizes spatial and channel attention to emphasize the features of suspicious lesion regions. Pathological relationship features, channel features and spatial features are integrated to achieve precise DR grading. Extensive experiments were carried out on the DDR, APTOS2019 and FGADR datasets. Results show that our method exceeds most existing approaches in classification accuracy and robustness, yielding superior classification performance.</p>