To address variations in perfume user experiences—ranging from olfactory perception, differing preferences for packaging design, inconsistent interpretation of the product‘s intended ambiance, to the distortive effects of excessive marketing leading to disparate user evaluations and an impaired comprehensive appraisal of the perfume‘s quality—this research introduces a human-machine collaborative Generative Adversarial Network (GAN).This innovative system incorporates a value network that rewards successful perfume formulations generated by the network and acknowledges expert perfumers in the discriminative network with increased remuneration and higher star ratings. A novel "Evaluation as Investment" algorithm is also devised wherein every assessment made by these experts, whether optimistic or pessimistic, is treated akin to a financial investment; positive reviews equate to buying long, while negative critiques parallel selling short. The actual user experience feedback then serves to recalibrate the experts‘ compensation and rating stars, thereby creating a self-regulating ecosystem. The efficacy of this evaluation methodology is demonstrated through real-world case studies, complemented by the development of a user-friendly mobile application designed for straightforward operation and easy comprehension. This tool is available for trial by interested companies, promising not only to ensure perfumes genuinely resonate with the public and enhance brand reputation, but also to incentivize highly skilled and impartial experts to partake in the judging process. Consequently, consumers develop heightened trust in professional perfumers, and perfume houses can amass a cadre of top-tier, highly-rated fragrance connoisseurs, fostering an environment where excellence and authenticity thrive.