In this study, a series of resonant-column experiments were conducted on marine clays from Bohai Bay and Hangzhou Bay, China. The characteristics of the dynamic shear modulus (G) and damping ratio (D) of these marine clays were examined. It was found that G and D not only vary with shear strain (γ), but they also have a strong connection with soil depth (H) (reflected by the mean effective confining pressure (σ_m) in the laboratory test conditions). With increasing H (σ_m) and fixed γ, the value of G gradually increases; conversely, the value of D gradually decreases, and this is accompanied by the weakening of the decay or growth rate. An intelligent model based on a back-propagation neural network (BPNN) was developed for the calculation of these parameters. Compared with existing function models, the proposed intelligent model avoids the forward propagation of data errors and the need for human intervention regarding the fitting parameters. The model can accurately predict the G and D characteristics of marine clays at different H (σ_m) and the corresponding γ. The prediction accuracy is universal and does not strictly depend on the number of neurons in the hidden layer of the neural network.