Model obfuscation refers to the equivalent transformation of neural networks into another form, which is an efficient and low-cost technique for protecting neural networks. To detect the flaws of model obfuscation, researchers have proposed model deobfuscation techniques in the hope of improving model obfuscation methods. However, model deobfuscation techniques are not fully explored, with limited applicability and effectiveness. Therefore, this study proposes a model deobfuscation method based on neural machine translation (NMT). This method models a deobfuscation task as a seq2seq task. It provides a more detailed sequential representation of the obfuscated model, identifies and processes the obfuscated information in the weight parameters, and utilizes an NMT-based model for deobfuscation translation. The experimental results demonstrate that this method addresses the shortcomings of existing methods, effectively capturing the obfuscation features and restoring the architectures of models. It can serve as a general solution to model deobfuscation.