White matter fiber bundle parcellation is crucial for understanding brain connectivity, yet faces challenges due to the enormous number of streamlines and the need for anatomically meaningful classification. Existing methods often struggle to utilize the sequential nature of streamlines efficiently and fail to balance local and global feature extraction, limiting their accuracy in complex white matter architectures. This paper presents the Streamline Signature Net, a novel deep learning framework that addresses these limitations. The key contributions include: (1) leveraging path signature transforms and a dual-branch network to encode the geometric and sequential properties of streamlines, (2) implementing multi-scale window slicing to extract both fine-grained local details and global trajectory patterns, and (3) introducing a dynamic attention mechanism to weight discriminative slices within streamlines. Comprehensive experiments were conducted on two public datasets: ORG-800, an 800-cluster white matter atlas, and 105HCP-72, a semi-automatically annotated dataset with 72 fiber bundle classes. Our proposed SSN achieved state-of-the-art performance(reaching 93.53% accuracy on ORG-800 and 88.96% on 105HCP-72). The results highlight SSN’s potential for advancing neuroanatomical research and clinical applications, including the diagnosis of neurodegenerative diseases and studies on brain development. The source code and implementation details of the SSN are publicly available at https://github.com/RenchZhao/Streamline_Signature_Net