Loop closure detection (LCD) is an indispensable part of simultaneous localization and mapping (SLAM) systems, making it significant for eliminating accumulative trajectory error and constructing a consistent map. Focusing on visual SLAM, we propose a LCD method using local-global similarity measurement strategies. As with most deep learning-based methods, we treat the LCD problem as a classification problem. We use a pretrained VGG-16 network to obtain advanced features and a multiscale feature extraction module to integrate the features of different receptive fields. Finally, we overcome the problems of dynamic object occlusion and large-area repetitive texture using a local-global similarity measurement module to measure the similarity between current and historical frames in all dimensions. This approach decouples feature extraction from classification determination, which enables us to use the advantages of both supervised and unsupervised learning to cope with the imbalance in the number of nonloop and loop pairs. Experimental results on three different datasets showed that the proposed method is significantly better than the state-of-the-art appearance-based LCD methods in dynamic urban, suburban, and highway scenarios.