In response to the issues with existing 3D point cloud object recognition algorithms based on deep learning methods, such as the lack of feature interaction between points in multi-layer perceptrons, reliance on Euclidean distance between point clouds, and failure to consider the correlation at the feature channel level, we propose an attention mechanism-based 3D point cloud(PAttenCls) object recognition algorithm. The spatial attention mechanism based on points is employed to explore the attention values between points, achieving adaptive neighborhood selection for point clouds; meanwhile, the channel attention mechanism based on points adaptively assigns weights to feature channels, enabling feature enhancement. Additionally, a geometric uniformization module is added to the network to address the different feature patterns of different local regions' geometric structures. The proposed algorithm achieves a recognition accuracy of 93.2% on the ModelNet40 dataset and an accuracy of 80.9% on the most difficult subset of the ScanObjectNN dataset, and its effectiveness is verified on real-world data. Experiments have proven that the proposed algorithm can better extract feature information from point clouds, making the point cloud recognition results more accurate.