Optically-sectioned structured illumination microscopy (OS-SIM) is broadly used for biological imaging and engineering surface measurement owing to its simple, low-cost, scanning-free experimental setup and excellent optical sectioning capability. However, the efficiency of current optically-sectioned methods in OS-SIM is yet limited for surface measurement because a set of wide-field images under uniform or structured illumination are needed to derive an optical section at each scanning height. In this paper, a deep-learning-based one-shot optically-sectioned method, called Deep-OS-SIM, is proposed to improve the efficiency of OS-SIM for surface measurement. Specifically, we develop a convolutional neural network (CNN) to learn the statistical invariance of optical sectioning across structured illumination images. By taking full advantage of the high entropy properties of structured illumination images to train the CNN, fast convergence and low training error are achieved in our method even for low-textured surfaces. The well-trained CNN is then applied to a plane mirror for testing, demonstrating the ability of the method to reconstruct high-quality optical sectioning from only one instead of two or three raw structured illumination frames. Further measurement experiments on a standard step and milled surface show that the proposed method has similar accuracy to OS-SIM techniques but with higher imaging speed.