Deep convolution neural network is superior to traditional manual features in the image feature representation, but it still has the problem that the calculation time increases with the increase of data in the loop-closure detection. In order to solve this problem, a fast closed-loop detection algorithm based on VGG16 model is proposed. The algorithm uses the VGG16 network model, which is pre-trained on ImageNet, to extract the image convolution features, and a loop-closure candidate frame is obtained by an adaptive particle filtering method to fix the operation time. The algorithm is tested in the mainstream loop-closure detection dataset City Centre and New College, and the experimental results show that the algorithm can achieve 70% recall rate under 92% accuracy and 61% recall rate under 96% accuracy on the two datasets, which exceeds the conventional algorithms, and effectively solves the problem of calculating time growth.