Abstract: Artificial social networks which include thousands of members have become an important platform for network opinion evolution. People will not try their best to get and consider all other people’s opinions when they give their opinions in Internet, because they do not have enough time and energy to do this, and they don’t think it is necessary. But in bounded confidence model, it needs to take into account all the other people’s opinions when any people update his opinion, which is in conflict with the real networks. To solve this problem, a novelty network opinion evolution model with dualchoices based on effect and confidence was proposed, according to human behavioral patterns in real networks, and a model of people’s opinion insistence strategy was made. The new model and the bounded confidence model with different sets of parameters were simulated for many times, and the results are in good agreement with what happened in real networks.

Key words: network opinion evolution, high-effect, priority, bounded confidence model, opinion insistence strategy