Summary: In real life, there are many complex multiple attribute group decision making (MAGDM) problems with high decision risk and uncertainty. The decision-making process of the complex MAGDM can encounter the following three problems: (1) Since different experts have different knowledge structures and interests, they master different individual attribute information of alternatives. (2) Experts may have different consensus degrees for alternatives under different attributes. (3) For some alternatives, the experts can not make an immediate decision in the actual decision-making process. The experts need much more information to decide on these alternatives in the subsequent decision step. To solve the problems as mentioned above, we propose sequential three-way multiple attribute group decision making (STWMAGDM) with individual attributes by introducing sequential three-way decisions. Meantime, we construct a multilevel granular structure based on the consensus degree of attributes. Further, at each granularity level, the experts need to reach consensus before deducing decision results. For improving the consensus reaching process, we take into account the social influence among experts with the aid of opinion dynamics. In this case, we construct social networks based on the similarity of experts and the amount of attribute information mastered by experts to describe the social influence. Meanwhile, we modify the model of opinion dynamics by introducing the interaction willingness of experts and establish the corresponding adjustment rules of interaction willingness. Finally, we use two diagnosis examples of breast cancer and heart disease to explain our model in detail. In order to verify the effectiveness of our method, we also perform the corresponding comparative experiments and sensitivity analyses.