Herein, we propose a convolutional neural network based on channel attention mechanism for multiscale feature fusion regarding the characteristics of LiDAR point clouds, such as the complex geometric structure and extreme scale variations among different categories, resulting in the issue of low classification accuracy of small targets. First, low-level features (planarity, linearity, normal vector, and eigen entropy) are calculated for each point by setting a spherical neighborhood, and they are fused with high-level features acquired by the network to improve the geometry awareness of the constructed model. Then, a multiscale feature fusion module is designed based on the channel attention mechanism to learn fusion weight coefficient so that the network can adapt to the receptive field size of different scale objects and realize different scales information filtering, which improves the classification performance of the small-scale object. According to the experiments, the average F₁ score using the ISPRS Vaihingen 3D Semantic Labeling benchmark is 72.2%. Compared with other algorithms, our model has the highest classification accuracy in the powerline and car categories with F₁ scores of 64.3% and 79.9%, respectively.