Autonomous bronchoscopic navigation is vital for pulmonary disease diagnosis and treatment but still suffers from subtle anatomical variations and open-set bronchial variants. Current vision-language foundation models enable open-set recognition but get trapped in capturing fine-grained spatial features and disentangling class-specific attributes. We propose a structure-aware cross-modal prompt tuning framework that combines the contrastive language-image pre-training (CLIP) model and the efficient segment anything model (EfficientSAM) to address these limitations. Specifically, EfficientSAM extracts structure-aware features for learnable textual prompts via cross-modal attention to enrich visual embeddings in CLIP, while a base-unknown decoupled head disentangles shared anatomical knowledge and class-specific features in the latent space, enhancing separability for both base and open-set classes. Moreover, unified optimization aligns multi-modal distributions using image-text matching loss and base-unknown decoupled loss. We evaluate our method on clinical bronchoscopic data, with the experimental results showing that our method outperforms state-of-the-art approaches and improves recognition and open-set identification (88.94%, 87.00%).