A point cloud semantic segmentation method for unstructured road scenes, represented by open-pit mining areas, is proposed to address issues such as harsh environmental conditions, blurred road boundaries, and significant differences in obstacle sizes. The method includes preprocessing, feature extraction networks, and inverse processing. Among them, preprocessing maps the three-dimensional point cloud to a two-dimensional Range View (RV) graph through coordinate transformation to improve network inference speed; The feature extraction network includes a convolutional attention module and a multi-scale residual module. The convolutional attention module is used to refine the segmentation boundaries and solve the problem of blurred road boundaries; The multi-scale residual module uses a large convolution kernel to expand the receptive field and fuse up and down sampling features to adapt to the problem of large changes in obstacle size in unstructured road environments; Inverse processing uses the K-nearest neighbor (KNN) algorithm to correct semantic labels and map point clouds back to three-dimensional space. The proposed method was tested on a typical unstructured road open-pit mining dataset, with an average intersection to union ratio of 85.1% and an inference speed of 6.423 ms. Compared with mainstream semantic segmentation network based on spherical projection, the overall accuracy was improved by 3%. In addition, the proposed method has been practically applied in unstructured road scenarios.