In deep learning-based point-cloud semantic classification, PointNet considers the three-dimensional coordinates of the point cloud as a direct input, however, the classification of irregular shape objects is a challenge. In this study, we propose a semantic segmentation network considering the normals of point cloud by adding a normal estimation module on PointNet. We estimate the normals using a principal component analysis method. Compared with the original model, the overall accuracy, mean per-class accuracy, and mean per-class intersection-over-union of the improved model are improved by 2.3 percentage points, 7.1 percentage points, and 3.9 percentage points respectively. Among the 13 semantic classes, the classification accuracy for 10 classes is improved, of which the classification accuracy of sofa and column is improved by 45.6 percentage points and 42.2 percentage points, respectively, and the mean per-class intersection-over-union is improved by 19.8 percentage points and 25.0 percentage points, respectively. Results show that the semantic segmentation network considering normals can improve the overall performance of the network to a certain extent and can significantly improve the classification effect of sofa and column.