In this study, we propose an algorithm for extracting the building feature information based on point cloud slices and minimum bounding rectangles to enhance the efficiency and accuracy of building feature information extraction. First, the algorithm denoised and sliced the point cloud horizontally and vertically. Then, the horizontal and vertical angle differences with respect to the adjacent points in each slice were used to automatically and quickly extract the contour points of the entire building, doors, and windows. Subsequently, the contour points were filtered, classified, and sorted. Finally, the overall sizes of the regular building and the information of windows and doors were extracted using the minimum bounding rectangle. In this study, three experiments were conducted to compare the extracted sizes of the entire building, windows, and doors with their actual sizes. The results denote that the information extraction accuracy with respect to windows, doors, and the entire building is within 3 cm, the overall accuracy is greater than 97.4%, and the time required to extract the information of approximately 1.6 million building point cloud data is less than 8 s, proving the effectiveness of the proposed algorithm.