In this paper, we propose a data dynamic network for data enhancement for bearing defect identifications. Nodes and edges of data are constructed for the establishment of a data dynamic network. Together with the feature fusion techniques, the energy, recurrence rates, and amplitude–frequency spectra are reconstructed in the time-delayed phase space for preparation of bearing defect identification. The accuracy for bearing defect identification reaches up to 98.5 %.