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Loop closure detection is a crucial part of VSLAM. However, the traditional loop closure detection algorithms are difficult to adapt to complex and changeable scenes. In this paper, we fuse Gist features, semantic features and appearance features of the image to detect the loop closures quickly and accurately. Firstly, we take advantage of the fast extraction speed of the Gist feature by using it to screen the loop closure candidate frames. Then, the current frame and the candidate frame are semantically segmented to obtain the mask blocks of various types of objects, and the semantic nodes are constructed to calculate the semantic similarity between them. Next, the appearance similarity between the images is calculated according to the shape of the mask blocks. Finally, based on Gist similarity, semantic similarity and appearance similarity, the image similarity calculation model can be built as the basis for loop closure detection. Experiments are carried out on both public and self-filmed datasets. The results show that our proposed algorithm can detect the loop closure in the scene quickly and accurately when the illumination, viewpoint and object change. [ABSTRACT FROM AUTHOR]