The invention discloses a decentralized distributed training topological structure, and the structure is characterized in that the structure is an n-dimensional hypersquare topological structure, is a closed, compact and convex undirected graph, and is composed of a finite non-empty node set and a finite edge set; a one-dimensional skeleton of the topological structure is composed of a group of line segments which are orderly arranged in a space where the one-dimensional skeleton is located and aligned with each dimension and are equal in length, wherein the opposite line segments are parallel to each other, and the line segments intersecting at one point are orthogonal to each other. The method focuses on decentralized distributed performance training, the training tasks are 'homogenized', the load of the training tasks is uniformly distributed to each training node in the distributed training system, the system performance does not depend on the performance of a single training node any more, and the method has the advantages of short iteration time consumption, data localization and higher communication effectiveness.