3D point clouds provide accurate 3D geometric information and are therefore widely used in fields such as robotics, autonomous driving, and augmented reality. Current point cloud classification models improve performance by continuously increasing the number of parameters. However, this trend leads to an increase in model complexity and computation time. To address these issues, a lightweight point cloud classification model named Point-PT is designed based on a biased attention mechanism. The model constructs a local feature aggregation module through simple positional encoding and linear layers to extract local features of the point cloud, and it embeds an offset attention mechanism to screen local features and extract key information. The experimental results show that when the model parameter number is 0.4 Mbit, the proposed method is 12 times faster than PointMLP, and the number of parameters is reduced to 1/32 of the original, with an overall accuracy of 92.9%. Moreover, when the model parameter number is 0.8 Mbit, the overall accuracy is improved to 93.9%, representing increases of 2.0, 0.7, 0.1, and 1.0 percentage points, compared with those of PointNet++, point cloud transformer (PCT), PointPN, and dynamic graph convolutional neural network (DGCNN), respectively. The results of this study validate that the proposed model has not only a higher correctness rate but also lower model complexity.