This paper proposes a modified evolving participatory learning (MEPL) method to identify T-S fuzzy systems from nonstationary streaming data. The proposed algorithm includes new rule adding and merging strategies as well as an improved initialization method for consequent parameters. Firstly, noticing the missing in the existing approaches where the useful output signals are not being used in rule adding, a new rule adding strategy utilizing both the input and output data is proposed. Secondly, a new rule merging strategy is proposed with the rule similarity and merging are decided by combining the similarities between the centers and shapes, which is different from the existing approaches that only use the similarity between the centers of membership functions to determine the merging of the fuzzy rules. Finally, a special method is proposed to determine the initial values of parameters in the consequent parts of the new added fuzzy rules. Numerical results indicate that MEPL can give accurate prediction results and often performs better than other state-of-art algorithms in accordance with accuracy.