To address challenges in component recognition， such as self-occlusion， unclear visual features， and substantial feature variation due to distance， a novel infrared target component recognition method based on a knowledge graph is proposed. This method utilizes a whole-to-component prediction （WCP） strategy to sequentially recognize target components. Initially， the overall target is detected， followed by an expansion of the target region to high resolution to enhance signal details. Subsequently， a component-related attention module （CAM） integrated with the knowledge graph exploits structural relationships among parts to infer visible interconnections and employs attention mechanisms to improve recognition performance， thereby mitigating issues arising from ambiguous visual features. For components affected by self-occlusion， a Self-Occlusion Learning Rate Decay （SLD） control strategy， based on self-removal capability， is introduced to strengthen the model's capacity to learn from occlusions and facilitate convergence. Validation is conducted using an indoor target equivalence scaling system， employing room-based models across various orientations and distances， with aircraft tested under diverse conditions， achieving an average precision of 92.2%. Experimental results demonstrate that the proposed method surpasses existing approaches in component recognition accuracy and recall， markedly enhancing both precision and recall metrics.