  The detection of nuclear spins using individual electron spins has enabled
new opportunities in quantum sensing and quantum information processing.
Proof-of-principle experiments have demonstrated atomic-scale imaging of
nuclear-spin samples and controlled multi-qubit registers. However, to image
more complex samples and to realize larger-scale quantum processors,
computerized methods that efficiently and automatically characterize spin
systems are required. Here, we realize a deep learning model for automatic
identification of nuclear spins using the electron spin of single
nitrogen-vacancy (NV) centers in diamond as a sensor. Based on neural network
algorithms, we develop noise recovery procedures and training sequences for
highly non-linear spectra. We apply these methods to experimentally demonstrate
fast identification of 31 nuclear spins around a single NV center and
accurately determine the hyperfine parameters. Our methods can be extended to
larger spin systems and are applicable to a wide range of electron-nuclear
interaction strengths. These results enable efficient imaging of complex spin
samples and automatic characterization of large spin-qubit registers.
