Tibetan medicine has received wide acclaim for its unique diagnosis and treatment methods. The identification of Tibetan medicinal materials, which are a vital component of Tibetan medicine, is a key research area in this field. However, traditional deep learning-based visual neural networks face significant challenges in efficiently and accurately identifying Tibetan medicinal materials, due to their large number, complex morphology, and the scarcity of public visual datasets. To address this issue, we constructed a computer vision dataset with 300 Tibetan medicinal materials and proposed a lightweight and efficient cross-dimensional attention mechanism, the Dual-Kernel Split Attention (DKSA) module, which can adaptively share parameters of the kernel in both spatial and channel dimensions. We then replaced the 3×3 convolutional kernel in the bottleneck blocks of ResNet with the DKSA module, obtaining an EDKSA block that can be embedded into various backbone architectures to enhance the model?s performance. Based on the EDKSA blocks, we developed a novel lightweight backbone architecture, EDKSANet, and demonstrated its competitive performance in image classification, object detection, and instance segmentation on the ImageNet and MS COCO public datasets. Moreover, EDKSANet achieved excellent classification performance on the Tibetan medicinal materials dataset, with an accuracy of up to 96.85%.