The group ranking approach has been applied in many applications, such as in decision-making support systems, group recommendation systems, and so on. Previous studies have focused on the use of static information at a specific time, however, changing information is closer to real life. For this reason, this study utilizes group information at different time-points for consensus sequence mining. The aim is to find the change in consensus sequence at different times so as to understand changes in group preference moving closer to the authentic idea of that group. Finally, this study classifies the change of group consensus sequence into five types, which allows decision-makers to visualize the changing thoughts of the customer community and make better strategies.