This paper proposes an unwrapping algorithm based on deep learning for inertial confinement fusion (ICF) target interferograms. With a deep convolutional neural network (CNN), the task of phase unwrapping is transferred into a problem of semantic segmentation. A method for producing the data set for the ICF target measurement system is demonstrated. The noisy wrapped phase is preprocessed using a guided filter. Postprocessing is introduced to refine the final result, ensuring the proposed method can still accurately unwrap the phase even when the segmentation result of the CNN is not perfect. Simulations and actual interferograms show that our method has better accuracy and antinoise ability than some classical unwrapping approaches. In addition, the generalization capability of our method is verified by successfully applying it to an aspheric nonnull test system. By adjusting the data set, the proposed method may be transferred to other systems.