At present, there are still problems of inaccurate labeling, inaccurate target matching, and excessively complex image processing of machine vision in spatial structure displacement measurement. Therefore, machine vision measurement technology is combined with deep learning algorithm to construct a precise displacement measurement model for large-span spatial structure of buildings based on machine vision technology and deep learning. First, the binocular stereo vision technology based on machine vision is expounded to analyze the deficiencies in measuring displacement of large-scale spatial structures. Second, related concepts of Convolutional Neural Networks (CNNs) are introduced, and You Only Look Once v2 (YOLOv2) is selected as the recognition algorithm of sphere joints in the long-span spatial structure of buildings. Finally, a displacement measurement system for long-span spatial structures is constructed based on CNN models and YOLOv2 algorithm. Images of various spatial structures collected through the Internet and shot by smartphones constitute the data set to test the performance of YOLOv2 recognition algorithm and three kinds of CNN models, namely the AlexNet model, Darknet19 model, and Resnet152 model. The experimental results demonstrate that the Recall of Darknet19 model is up to 93.9%, and the Intersection Over Union (IOU) is 82.02%, which are at least 3.6% and 4.4% higher than those of the other two models, respectively. Besides, the Darknetl9 model performs better in the task of identifying and locating sphere joints in spatial structures. In addition, when the Darknet19 model is trained about 20,000 times, the Recall of the YOLOv2 recognition algorithm reaches 94.69%, and the IOU attains 84.98 %. Moreover, the average recognition accuracy of YOLOv2 algorithm is 2.3% higher than other recognition algorithms, which has a high recognition level. Furthermore, the relative error of displacement measurement in the single direction of each stage of the measurement model designed here is controlled within 12%, which can meet the requirements of displacement measurement of spatial structure. The purpose of this work is to provide essential technical support for the upgrading of displacement measurement technology of large-span spatial structure of buildings and the improvement of measurement accuracy.