In order to timely identify the changing trend of marine environment and reduce the influence of long-term accumulated marine environment data on prediction model,an online prediction model of marine environment data based on recurrent online sequential extreme learning machine (R-OSELM) is proposed.The marine environment data training set is initialized by an online method,the existing marine environment data is input block by block via online sequential extreme learning machine algorithm,and the input weight is cyclically processed by automatic coding technology of extreme learning machine and a normalized method,which realize the online update of the prediction model.Finally,online prediction of marine environment data is completed.The model is then used to predict dissolved oxygen,chlorophyll A,turbidity,and blue-green algae.The results show that the prediction accuracy of R-OSELM model is better than that of the comparison model.It is verified that the proposed R-OSELM model is capable of online prediction of marine environment data,which can provide support for early warning of marine eutrophication and other marine environmental pollution.