Echo state networks (ESN) have received extensive attention because of their superior performance in nonlinear time series forecasting tasks. Most of the research on ESN focuses on extracting features from time series and improving the activation function to enhance prediction accuracy. In this study, we creatively propose recombining the obtained features to improve the prediction accuracy further. To this end, two state reconstruction methods are developed based on the existing features in the ESN. Additionally, ESN is very sensitive to the setting of hyperparameters, resulting in its unstable performance of time series prediction. Thus, we adopt the Self-Normalizing Activation (SNA) function to reduce the model’s sensitivity to hyperparameters and improve its stability. Then our proposed model is compared with the popular improved ESN models like DeepESN, GroupESN, and Grouped DeepESN on four time series datasets. The results show that our model has the best prediction performance.