When robots conduct simultaneous localization and mapping (SLAM) tasks in large-scale scenes, there is serious mismatching or missed matching in loop-closure detection. Focused on this problem, this study proposes a new closed-loop detection algorithm based on a residual network (ResNet) to extract features of image sequences. The global features of an input image are extracted using a pretrained ResNet. The features of the frame image and previous image sequenced with a certain length are stitched by the down sampling method, and the results are taken as the features of the current frame image to ensure the richness and accuracy of the image features. Then, a double-layered query method is designed to obtain the most similar image frame, and the consistency of the most similar image is checked to ensure the accuracy of the loop-closure. The proposed algorithm can achieve an 83% recall rate under 100% accuracy and an 85% recall rate under 99% accuracy in the loop-closure detection mainstream public datasets of New College and City Centre, which is significantly improved compared with the traditional bag of words method and VGG16 method.