To capture 3D geometric details, point cloud understanding primarily employs convolution, dynamic map, or transformer methods to build complex local geometry extractors. When dealing with sparse and noisy point clouds in real-world environments, the structural design of these methods tends to be complex and perform poorly. In this paper, we propose a lightweight point cloud understanding framework based on Laplacian feature convolution, which has high accuracy on normal-sized point cloud datasets and performs well when dealing with sparse and noisy point cloud inputs. The adaptive Laplacian convolution module combined with the vector attention mechanism can effectively capture the local details of point cloud geometry. The framework employs a multi-scale feature fusion mechanism between modules to reduce the number of sampling points step by step, reducing redundant computations and achieving a lightweight effect. In addition, we propose a parameter-free module for interpolation to enhance the training part. Experimental results on object-level datasets show that the lightweight method proposed in our paper has similar accuracy to the current best method in terms of accuracy (Acc) and mean intersection over union (mIoU), and has better robustness in handling sparse and noisy point clouds.