To expand the decryption method of computational holographic encrypted images for a symmetric-asymmetric hybrid encryption system that cannot be easily attacked illegally， a scheme involving the use of a neural network to restore chaotic iris phase mask computational holographic encrypted images is proposed. First， a plaintext image is encrypted and a ciphertext image of a computational hologram is generated. Next， numerous ciphertext image pairs are generated as datasets. Subsequently， they are continuously trained and tested by using them to build a neural network. Results show that the trained neural network can fit the mapping relationship from the ciphertext image to the plaintext image and that the public or private key is no longer used to decrypt the ciphertext image during decryption. Additionally， the average cross-correlation coefficient is 0.984， the average peak signal-to-noise ratio is 61.0 dB， and the average structural similarity is 0.77， which indicate better performances compared with the performances of a plaintext image recovered by the neural network. By polluting the ciphertext image with noise， a higher quality image is obtained. The purpose of decrypting a ciphertext image via a neural network is achieved， and the scheme is shown to be feasible and robust.