LiDAR exhibits high non-ground point ratios and uneven density distributions when obtaining point-cloud data in areas with steep terrains and dense vegetation coverage. Classical filtering algorithms cannot readily obtain accurate point-cloud filtering results. In point-cloud filtering using deep learning, issues such as insufficient information utilization and inadequate feature extraction persist. Therefore, this study proposes a point-cloud filtering network that integrates multidimensional features and global contextual information (MGINet). It establishes a framework for multidimensional feature extraction and global information fusion to enhance the accuracy of point-cloud filtering in complex mountainous regions. MGINet begins by designing a local cross-feature fusion module, which combines normal vectors with spatial geometric structures to extract high-dimensional diverse features, thereby preserving the local spatial structure features of the point cloud. Subsequently, a global-context aggregation module is introduced to capture global contextual information, thus enhancing the generality of the features through cross-coding. Finally, experimental testing on both public and actual datasets from complex mountainous areas shows that MGINet outperforms classical algorithms in terms of point-cloud filtering accuracy.