In order to timely identify the changing trend of marine environment and reduce the influence of long-term accumulated marine environment data on the prediction model, an online prediction model of marine environment data based on cyclic online sequential extreme learning machine was proposed. The marine environment data training set is initialized by online method, the existing marine environment data is input block by block by 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, and the prediction model is updated online. Finally, online prediction of marine environmental data is completed. The model was 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 confirmed that R-OSELM model has the online prediction ability of marine environmental data, which can provide reference for marine eutrophication and marine environmental pollution early warning.