An improved Inception Transformer with Bi-Level Routing Attention and multi-scale feature collaborative enhancement for Chinese ancient architecture image classification

Chinese ancient architecture embodies China’s cultural heritage but remains challenging to classify because of spatially dispersed key components, high inter-class similarity, and multi-scale visual characteristics. To address these challenges, we propose BDM-ViT, an improved Vision Transformer model based on the Inception Transformer. Bi-Level Routing Attention enhances the long-range semantic interactions among highly relevant semantic regions through region-level sparse routing, while a Dual-Dimension Feature Collaborative Enhancement module jointly refines channel and spatial features. A Multi-Kernel Feed-Forward Network captures fine-grained textures and global structurals, and a Joint Discriminative Loss improves classification through boundary optimization, hard-sample learning, and intra-class feature constraints. We also construct the Chinese Ancient Architecture Dataset(CAAD), containing 18 architectural categories. Comparative experiments show that BDM-ViT significantly outperforms state-of-the-art convolutional neural networks and Vision Transformer models, achieving 95.46% accuracy with superior precision, recall, and F1-score. Ablation experiments and visualization analyses further verify the effectiveness and complementarity of each proposed module.