Long-line river regulation projects such as bank lining and revetment projects are far stretched and scattered with a wide range, resulting in inconvenient transportation for inspection. Besides, conventional manual inspection is time-consuming and labor-intensive, which is difficult to grasp the construction progress of the whole project in practice. In view of this, this paper proposes an intelligent construction progress monitoring method of river regulation projects based on unmanned air vehicle (UAV) inspection and deep learning, which can calculate the construction progress by locating construction nodes (i.e., starting point and ending point of the construction area). Firstly, the object detection model of construction area is established to recognize the construction area of lining and revetment and locate the construction nodes based on UAV aerial photography. Then, SIFT (scale-invariant feature transform) algorithm is used to match the construction nodes in different video frames, and a motion parallax method based on monocular vision is introduced to locate the actual work area coordinates of construction nodes. Finally, the current lining construction progress is calculated and the progress deviation is analyzed. The results show that the average error of the construction progress is 1.026 m, and the average relative error is 0.74 %, indicating that the proposed method can recognize the construction progress of lining and revetment accurately based on the UAV images, thereby achieving full coverage and rapid inspection of long-line projects, timely control of on-site construction progress, and improvement of the intelligent level of engineering management.