The invention discloses a laser SLAM (simultaneous localization and mapping) closed loop detection method based on deep learning. According to the method, an SLAM closed loop detection problem is converted into an SLAM data sample search problem; a deep Hash network for carrying out Hash coding on laser point cloud samples is established innovatively; and sample similarity calculation is carried out based on the Hash coding, thereby realizing similar sample rapid search and SLAM closed loop detection. According to the established deep Hash network, through utilization of the advantages of the deep learning, binary coding more reliable than a traditional coding technology can be obtained, so the search accuracy is greatly improved. A new point cloud characteristic extraction algorithm is designed, through application of the algorithm, on the basis of carrying out space division and projection on point clouds, a characteristic graph is established, laser point cloud sample data is reliably described, and effective docking with the deep Hash network is realized. According to the established deep Hash network, through offline training, a trained model can satisfy real-time coding demands.