The embodiment of the invention provides a clustering method of multi-source heterogeneous data, which comprises the following steps of: aiming at the multi-source heterogeneous characteristic of a characteristic space, fusing the characteristic space to construct more than one object tensor and characteristic space combination vector; obtaining a corresponding feature transfer tensor according tothe object tensor; processing the feature transfer tensor by adopting a preset multi-relation attribute combination ranking algorithm to obtain a corresponding attribute combination ranking tensor; decomposing the object tensor and the attribute combination ranking tensor by adopting a preset high-order singular value decomposition algorithm to obtain a corresponding core tensor and a factor matrix; calculating according to the feature space combination vector, the core tensor and the factor matrix to obtain a corresponding object similarity matrix; and performing clustering according to theobject similarity matrixes under different feature spaces to obtain a multi-clustering result. The method solves the problem that the clustering result is difficult to interpret in the prior art.