This letter proposes a novel neural network architecture, Dendritic Learning-Incorporated Vision Transformer (DVT), which integrates dendritic learnable network architecture with Vision Transformer to enhance image recognition accuracy. Inspired by dendritic neurons in neuroscience, the DVT combines a Vision Transformer for feature extraction and a dendritic network for feature classification. The Vision Transformer processes images into multiple patches using self-attention mechanisms, while the dendritic network, with its synapse, dendrite, and soma layers, ensures accurate feature classification. Extensive experiments on various benchmarks, including CIFAR10, SVHN, CIFAR100, and Tiny-ImageNet, demonstrate that DVT outperforms state-of-the-art methods, particularly in handling complex nonlinear classification problems. The study also highlights the importance of biological interpretability in improving both performance and understanding of visual perception mechanisms.This letter proposes a novel neural network architecture, Dendritic Learning-Incorporated Vision Transformer (DVT), which integrates dendritic learnable network architecture with Vision Transformer to enhance image recognition accuracy. Inspired by dendritic neurons in neuroscience, the DVT combines a Vision Transformer for feature extraction and a dendritic network for feature classification. The Vision Transformer processes images into multiple patches using self-attention mechanisms, while the dendritic network, with its synapse, dendrite, and soma layers, ensures accurate feature classification. Extensive experiments on various benchmarks, including CIFAR10, SVHN, CIFAR100, and Tiny-ImageNet, demonstrate that DVT outperforms state-of-the-art methods, particularly in handling complex nonlinear classification problems. The study also highlights the importance of biological interpretability in improving both performance and understanding of visual perception mechanisms.