Developing a precise parathyroid identification model is difficult due to the intricacies of thyroid surgical environments, including obstruction of surgical instruments, light variations, extrusion, and deformation. Given that traditional detectors (such as Faster R-CNN) rely on limited receptive fields, they cannot effectively solve the problems of occlusion, deformation, and data scarcity in PG detection. This research presents an adaptive capsule graph neural network (ALCapsule-GNN) for parathyroid gland (PG) detection, a new model that integrates capsule networks with graph convolutional networks (GNN) to improve the efficacy of PG object detection tasks in medical images. The model employs a capsule network-based Backbone to extract multi-scale features and constructs an adaptive graph using a dynamic graph generator, where nodes represent capsules and edges encode spatial-semantic relationships. The hierarchical graph capsule network incorporates an attention mechanism to refine the graph structure while leveraging a shared weight mechanism to minimize redundant computations. Iterative message passing enhances target detection performance and improves the robustness to occlusion and deformation. ALCapsuleGNN efficiently captures spatial relationships and contextual information among objects while preserving the model’s computational efficiency and adaptability. Comprehensive trials in a proprietary parathyroid data set demonstrate that our method achieves exceptional results in automated parathyroid detection.