Point cloud segmentation is crucial for key tasks, including intelligent driving, object recognition and detection, as well as reverse engineering. PointNet represents a direct point cloud data processing approach widely utilized in point cloud segmentation tasks. Nevertheless, it is associated with low segmentation accuracy and the computational cost of PointNet++ is high. Aiming at the above problems, an algorithm combining DenseNet and PointNet was proposed for the segmentation of point clouds. A three-branch hybrid attention mechanism was introduced to enhance PointNet capability to extract local features. DenseNet-STN and DenseNet-MLP structures were proposed to substitute spatial transformation networks (STNs) and multi-layer perceptrons (MLPs) in PointNet, in line with the dense connected convolutional networks (DenseNet) concept. At the same time, the add connection in DenseBlock, rather than the Concat connection, to enhance the accuracy of point feature correlation, without imposing significant complexity to the model. DenseNet-PointNet demonstrates effective generalization ability for complex classification problems and facilitates better function approximation, thereby improving the precision of point cloud segmentation. The findings of the effectiveness and ablation experiments show that the proposed algorithm performs well. The results of the point cloud segmentation experiments indicate that the intersection and concatenation ratio (IoU) of DenseNet-PointNet is superior to that of PointNet in most categories, and also higher than that of PointNet++ in some categories. DenseNet-PointNet achieves this with only 47.6% of the parameters of PointNet++, and 49.1% of the floating point operations (FLOPs). Therefore, these experimental results confirm the feasibility and availability of DenseNet-PointNet.