Opinion formation has been studied a lot in complex network's framework recently. Among most of these models, the problem how to reach consensus state is studied as an outcome of dynamics on social network with static structure. Newman has put forward a co-evolution model in which opinions and social network adapt to each other with a global mechanism, where a phase transition emerges. Based on Newman’s model, two new local mechanisms, which are more close to the reality, are raised in first part of our work, a continuous phase transition of the structure of final consensus state emerges by tuning the only parameter in the model. Universal scaling function analyses and dynamic behavior of new models are discussed in details. However, there is lack of quantitative measures to describe what has happened during the evolution, except that the concept of “magnetization” borrowed from physics field which used to measure whether the system has achieved the consensus state in opinion model. In the second part of our work, “hierarchic social entropy” measuring diversity is applied to describe the process of different opinions distributing randomly initially emerge into several large and small communities finally, each of which holds the same opinion under co-evolution rules. The connection between the trend of hierarchic social entropy changing and the parameter in the models are studied.

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