  In trapped ion system, accurate thermometry of ion is crucial for evaluating
the system state and precisely performing quantum operations. However, when the
motional state of a single ion is far away from the ground state, the spatial
dimension of the phonon state sharply increases, making it difficult to realize
accurate and mode-resolved thermometry with existing methods. In this work, we
apply deep learning for the first time to the thermometry of trapped ion,
providing an efficient and mode-resolved method for accurately estimating large
mean phonon numbers. Our trained neural network model can be directly applied
to other experimental setups without retraining or post-processing, as long as
the related parameters are covered by the model's effective range, and it can
also be conveniently extended to other parameter ranges. We have conducted
experimental verification based on our surface trap, of which the result has
shown the accuracy and efficiency of the method for thermometry of single ion
under large mean phonon number, and its mode resolution characteristic can make
it better applied to the characterization of system parameters, such as
evaluating cooling effectiveness, analyzing surface trap noise.
