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　(gamma),　but　they　also　have　a　strong　connection　with　soil　depth　(H)　(reflected　by　the　mean　effective　confining　pressure　(sigma(m))　in　the　laboratory　test　conditions).　With　increasing　H　(sigma(m))　and　fixed　gamma,　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　(sigma(m))　and　the　corresponding　gamma.　The　prediction　accuracy　is　universal　and　does　not　strictly　depend　on　the　number　of　neurons　in　the　hidden　layer　of　the　neural　network.