In order to improve the measurement accuracy, stability and efficiency of the existing 3D coordinate positioning technology, a deep-learning-based point-diffraction interferometer for 3D coordinate measurement method was proposed. A deep neural network was designed for coordinate reconstruction of the point-diffraction interference field. The phase difference matrix was used as the input to construct the training dataset, and the coordinates of point-diffraction sources were used as the output to train the neural network model. The well-trained neural network was used to process the measured phase distribution initially and the phase information was converted to the coordinates of point-diffraction sources. According to the obtained coordinates of point-diffraction sources, the initial particles of the particle swarm optimization algorithm were further modified, and then the high-precision three-dimensional coordinate was reconstructed. This neural network provides a feasible method to establish the nonlinear relationship between the phase distribution of the interference field and the coordinates of the point-diffraction sources, and significantly improves the accuracy, stability and measurement efficiency of the 3D coordinate positioning. In order to verify the feasibility of the proposed method, numerical simulation and experimental verification were carried out, and different methods were used for repeated comparison and analysis. The results show that the single measurement time of the proposed method is about 0.05 s, and the experimental accuracy can reach the submicron magnitude. The mean and RMS values of the repeatability experiments are 0.05 μm and 0.05 μm, respectively, which proves the feasibility of the proposed method and its good measurement accuracy and stability. It provides an effective and feasible method for 3D coordinate positioning.