With the rapid advancement of intelligent synthetic voice technology, Al applications in the audiobook industry are injecting new vitality into the field. But does a more human-like Al voice necessarily make audiobooks more appealing to consumers? This study explores this question by analyzing audiobooks on the Himalaya platform. It extracts both low-and high-dimensional audio features from collected audio samples and employs random forest and neural network models to predict whether the audiobook narration originates from a human or Al. Building on these machine learning models, the study constructs an index to quantify the degree of anthropomorphism in audiobook voices. It thoroughly examines the underlying mechanisms of this index from technical and behavioral perspectives, shedding light on how it captures the similarities and diferences between human and Al - generated audiobook narration. Additionally, the paper investigates the relationship between the anthropomorphism index and consumer behavior. Empirical results reveal the presence of an uncanny valley elect: as the degree of anthro-pomorphism increases, audiobook ratings initially rise but subsequently decline. Interestingly, this effect is not observed in the volume of user comments. This research provides both theoretical and empirical insights into consumer acceptance of Al audiobooks, offering a fresh perspective on the evolving dynamics of human—AI interaction in the audiobook market.