This book systematically explores theories related to linguistic computational models and group decision making methods under uncertainty. It introduces innovative linguistic computational models capable of fusing complex linguistic information, including multi-granular linguistic information, unbalanced linguistic information and hesitant fuzzy linguistic information. Building upon the linguistic computational models, this book presents methods tailored to various types of group decision making problems under uncertainty. Additionally, it delves into group decision making problems where the personalized individual semantics of experts are considered. The book also showcases practical applications of the proposed group decision making methods, ranging from ERP system supplier selection to talent recruitment, subway line selection, and location selection for electric vehicle charging stations. By shedding light on novel models for modeling complex linguistic information and introducing new approaches to addressing linguistic group decision making challenges, this book offers valuable insights for engineers, researchers, and postgraduates interested in decision analysis, operations research, computational intelligence, management science and engineering, and related fields.