Laser shear speckle interferometry is an optical measurement technique that can measure the derivative of physical surface displacement. It is widely used in fields such as non-destructive testing and precision measurements. In laser shearing speckle interferometry, accurate acquisition of phase information is crucial for measuring the morphologies and surface features of targets. However, phase information is often affected by factors such as noise and nonlinear distortion. A speckle interferometric phase unwrapping method based on UCNet is proposed to address these factors. In this study, with U-Net employed as the framework, parallel symmetric convolutions and multiscale decoders were introduced into the network to improve the model's ability to understand and utilize feature information at different scales. Simultaneously, the SmoothL1Loss loss function was utilized to enable the model to adapt to tasks at different scales. Datasets for network training were used to simulate and test the generated network model, and actual collected phase maps were used to verify the accuracy and generalization ability of the network. Results show that the structural similarity index of the UCNet network is 1.05 times higher than that of the deep learning phase unwrapping network, and it can accurately achieve laser shear speckle interferometry phase unwrapping.