The paper studies an empirical study on the temsporal Characteristics underlying online user comments, with a focus on consumer behavior in mobile e-commerce platform. By the human dynamics, a method used to describe consumer behavioral patterns and the study investigates two key actions in the consumer journey: purchasing and commenting. Utilizing a large data-set obtained by Python web crawler, the research analyzes three core temporal characteristics—time interval, burstiness index, and memory index, associated with post-purchase comments. It explores when consumers are likely to share their shopping experiences online after making a purchase and uncovers the underlying formation rules of these behaviors. The findings reveal that the temporal patterns of “purchase-comment” behaviors follow a power-law distribution, offering new empirical insights into online human behavior dynamics and providing a deeper understanding of how consumers interact within digital commerce platforms.