Objective: Consumers' purchase decisions regarding new energy vehicles (NEVs) constitute a complex system, yet existing studies predominantly use static models that overlook the causal hierarchy and dynamic feedback among influencing factors. This study aims to fill this gap by developing an integrated model to systematically reveal the dynamic interrelationships affecting Chinese consumers' willingness to purchase NEVs. Methods: Employing a mixed-methods design, this study surveyed 728 residents in Zhengzhou, China. First, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method was used to analyze the causal hierarchical structure of eight core influencing factors. Next, a Fuzzy Cognitive Map (FCM) model was constructed to dynamically simulate the evolution of consumer cognition driven by key factors, with findings validated through in-depth interviews. Findings: The study identified the causal hierarchy of factors: intelligence (smart features), policy subsidies, charging speed, and driving range are fundamental "cause factors" that actively drive system changes; whereas vehicle price, comfort, and others are passive "effect factors." Among these, "vehicle price" serves as the central hub of the entire decision system, while "intelligence" is the strongest driving force. FCM simulations revealed that enhancing cause factors dynamically increases consumers' price sensitivity and environmental awareness. Additionally, highly educated and high-income groups currently dominate the market, and "driving range" remains the primary pain point for existing owners. Implications: This research offers a new dynamic analytical paradigm for understanding technology adoption behavior. It recommends that government policies shift from universal subsidies toward targeted support for core technology development and infrastructure construction. Enterprises should prioritize R&D investment in fundamental driving factors such as "intelligence.".