Objectives Low sampling rates on reconstruction surfaces cause high reconstruction error in near-field acoustic holography. Therefore, a deep learning-based approach which is applicable to planar sound sources and high-precision reconstruction with low sampling rates is put forward.

Methods A three-dimensional N-shaped convolution neural network for near-field acoustic reconstruction is established to extract features in the frequency dimension in order to make up for sparse sampling in the spatial dimension. A frequency focal mechanism, namely an adaptive frequency weight focus mechanism, is put forward to improve reconstruction precision in the natural frequency and high frequency. Moreover, this paper also raises frequency-scaled focal loss and frequency-scaled focal Kirchhoff–Helmholtz （KH） loss, which are considered regularization. To validate the proposed methods, datasets are created with COMSOL Multiphysics and Matlab.

Results The mean error range of 100–2 000 Hz of the algorithm proposed in this paper is only 4.96%, higher than those of SRCNN and PV-NN.

Conclusions The proposed method is verified as having the potential to reconstruct the accurate velocity fields of sound sources under low sampling rates.