To overcome the lack of local features of the deep neural network PointNet and the need for the improvement of segmentation accuracy, the present research introduces a local feature extraction method combined with an improved K-nearest neighbor (KNN) algorithm based on PointNet and a neural network known as KNN-PointNet. First, the local area is divided into k circular neighborhoods, and weights are determined according to the difference in the distribution density of sample data in the local area to calculate the classification of the points to be measured. Second, the local neighborhood features combined with single point global features are used as input for feature extraction by adjusting the network depth to extract local features for enhancing the correlation between points in the local neighborhood. Finally, the improved KNN algorithm is applied to the KNN-PointNet point cloud segmentation network for experimental comparison. Results show that compared with some current advanced segmentation networks, the segmentation network KNN-PointNet with local features extracted by the improved KNN algorithm has higher segmentation accuracy.