<p>The Z-number framework represents a significant advantage in fuzzy set theory since it encapsulates the reliability levels and ambiguity restrictions, and Complex Fuzzy Sets (CFSs) can describe the time-varying nature of information. Therefore, this study introduces the novel concept of Linguistic Complex Fuzzy Z-number (LCFZN), which is more adaptable and practical than numerical assessment and allows for the simultaneous representation of reliability measures and phase information. Then, this paper develops two aggregation operators, denoted as the LCFZN Sugeno–Weber Weighted Hamy Mean (LCFZNSWWHM^(<i>k</i>)) operator and the LCFZN Sugeno–Weber Weighted Dual Hamy Mean (LCFZNSWWDHM^(<i>k</i>)) operator, and offers unique properties of these operators, namely idempotence, monotonicity, and boundedness. Subsequently, an automobile purchase decision approach under the role of online reviews is proposed, which involves a construction process of the LCFZN evaluation environment that can effectively capture multidimensional consumer perceptions based on online review information features. As a Large Language Model (LLM), the General Language Model-4-Air (GLM-4-Air) is employed in the process of sentiment analysis of online reviews to obtain the emotional level. Finally, the proposed strategy is implemented for the purchase decision of New Energy Vehicles (NEVs), and through sensitivity analysis with various parameter configurations and comparative analysis with conventional methods, the approach demonstrates superior adaptability and decision consistency.</p>