## 1 Introduction  
As the ocean is measured by human beings, all countries worldwide have attached much importance to marine research. The prediction of marine elements plays a significant role in various ocean-related fields such as marine fisheries, marine aquaculture, and maritime communications [1, 2, 3]. Among these ocean-related elements, sea surface temperature (SST) plays a vital role in regulating climate and its changes [4]. Therefore, it is well suited to monitoring climate change. Various climate phenomena such as ENSO(El Nino-Southern Oscillation) are closely associated with the changes in sea surface temperature [5]. In addition, SST could have a significant effect on the growth of marine organisms and the distribution of fishery resources [6, 7]. In this sense, it is vitally important to predict the sea surface temperature in an accurate and effective way. Many methods have been proposed to forecast SST. However, the time series data in SST involve complex coupling mechanisms and spatio-temporal relationships, thus making the data vary significantly in some specific time series. Moreover, the rules of data in different spaces also differ, which makes predictions of events challenging. For example, the sea surface temperature is affected by the external environment, such as ocean currents and salinity. The external environment causes uncertainty in the prediction of sea temperature. Conventional models often predict the global characteristics of the data but ignore the existence of the special data, which leads to some inaccurate predictions. Because the predictive analysis using a single prediction model for the entire dataset often obtains the overall characteristics of all data rather than training the predictions in a more targeted way, the data errors in the single prediction model are different. The data with a smaller error, mean that the characteristics of this part of the data are similar to the characteristics of the overall data. On the contrary, the data with a larger error, mean that the characteristics of this part of the data are inconsistent with the overall characteristics. Based on this analysis, a mechanism is required to train different models on the data with different characteristics. In this paper, we learn from the idea of ensemble learning to propose a cyclic evolutionary network model (CENS), which trains the original dataset in a data-driven manner. Since the traditional prediction models obtain different errors when predicting ocean elements at different times and locations, this model uses the prediction error of the traditional prediction model to extract the data with different features. In addition, the model trains several networks to form a network group for prediction, thus avoiding the problem that ensemble learning methods cannot handle special data with a single model. The model divides the data into multiple stages through training errors, partitioning a dataset with different features each time, and eventually training all the datasets with their corresponding networks. Different regions of data can be matched to different networks, which can improve the prediction accuracy of the model. At the same time, a cluster can be constructed from the results of multi-model data classification. The contributions of the paper are detailed as follows:  
The proposed model can identify the characteristics of data in complex sea areas. By using the prediction error of the network, the model is able to extract implicit features from the data. The model is a multi-network model obtained by means of data partition. Unlike traditional methods, which require such methods as DTW (Dynamic Time Warping) to segment the data, this is an end-to-end model. The data from different regions can be automatically selected to the corresponding networks. Multiple networks make predictions, respectively, for the data with different regions, which improves the accuracy of predictions. The effectiveness of the model has been fully verified in actual data.