Although Transformers have made significant progress in 3D point cloud processing， efficiently and accurately learning valuable low-frequency and high-frequency information remains a challenge. Moreover， most existing methods focus primarily on local spatial information， neglecting global spatial information， which leads to information loss. This paper proposes a novel point cloud learning network， referred to as the multi-scale dual-branch dual-attention network. First， in the feature extraction process of the point cloud， compared to methods that search for neighboring points at a fixed scale， the multi-scale K-nearest neighbor（KNN） approach not only preserves local structural details but also more effectively captures global geometric information. Second， this paper introduces a dual-branch dual-attention architecture to extract different spatial features， proposing a dual-attention mechanism combining local window attention and global channel content attention to extract low-frequency and high-frequency information from the network， respectively. Additionally， on this basis， this paper introduces the group-rational Kolmogorov-Arnold （GR-KAN） layer into the classification head， replacing the traditionally used multilayer perceptron （MLP） layer， which allows for more flexible handling of nonlinear features and makes the network more sensitive to complex datasets. Finally， extensive experiments demonstrate that the proposed model achieves an accuracy of 93.8% on the ModelNet40 dataset and 86.5% on the ScanObjectNN dataset， showcasing its superior performance and broad application prospects in 3D point cloud processing.