In order to meet the high-precision， high-efficiency， and anti-interference detection requirements of point diffraction interference measurement for phase unwrapping algorithms. Atrous Spatial Convolutional Networks -based phase unwrapping method for phase-shifted point diffraction interference images was proposed. By combining the autoencoder structure and the adaptive spatial convolution， higher phase unwrapping accuracy was achieved， and the degradation of the network model was effectively prevented， realizing controllable multi-scale feature extraction of the wrapped phase image. A large and diverse dataset of point diffraction phase data was used for training and optimization， which accurately identifies the order of the wrapped phase and quickly processed the wrapped image to obtain high-precision unwrapping results. The proposed method was applied to actual point diffraction interference images and compared with results from ESDI professional interference image processing software and other unwrapping algorithms. The results show that the unwrapping results have an RMSE value of 0.022 2 rad compared to the software processing results， with a surface fitting result PV difference of only 0.012 1λ and an RMS difference of only 0.004 2λ. In terms of time efficiency， it takes only 0.035 s on average to complete the processing of an image， while the traditional methods are all greater than 1 s. Compared to other methods， the proposed method exhibits fast and high-precision characteristics in unwrapping wrapped phase， providing a new feasible solution for high-precision phase unwrapping in point diffraction interference image processing.