Current zirconium alloy cladding corrosion models are mainly semi-empirical and show significant dispersion when compared to measured data. This study introduces neural ordinary differential equation (neural ODE) to model corrosion behavior, utilizing data-model fusion approach for network training. Initially, a semi-empirical model for zirconium alloy cladding corrosion is established through differential evolution algorithm, generating a large dataset for pre-training the neural network. The network is then fine-tuned using measured data. These methods effectively address the challenges of sparse cladding corrosion data and data available only at fixed time points, resulting in a more accurate model. The results show that the differential evolution algorithm can identify a set of appropriate parameters for the semi-empirical model, achieving a standard deviation of 0.040. The neural ODE model demonstrates even higher accuracy, reducing the standard deviation to 0.031 and improving accuracy by approximately 25%. Additionally, the model demonstrates excellent generalization capacity on other time points and new power histories.