The weakly supervised aircraft fine-grained detection task remains challenging due to the lack of fine-grained category labels. In addition, the high inter-class similarity among aircraft categories and complex background interference further hinders discriminative feature learning. To address these challenges, we propose a weakly supervised fine-grained aircraft detection framework based on prior-knowledge prototype learning. Specifically, a Prior-knowledge-based Soft-label Generator (PSG) is designed to exploit geometric and semantic priors, enabling the propagation of fine-grained category annotations from a few annotated samples to large-scale unlabeled data through reliable soft-label generation. To alleviate category ambiguity caused by highly similar aircraft categories, a Text Prototype Alignment (TPA) strategy is introduced to align vision-language semantic knowledge with the remote sensing domain and construct discriminative semantic prototypes. Furthermore, a Feature Purification Module (FPM) is developed to suppress background noise through morphology-guided feature purification and enhance aircraft-specific representations. Experiments on the FAIR1M-Aircraft dataset demonstrate that the proposed method achieves 30.5% mAP with only 50 fine-grained annotated images, outperforming the baseline by 10.1 mAP points.