With the rapid development of e-commerce, impulse purchases have become a significant driver of revenue growth for e-commerce platforms. Traditional research often relies on questionnaire surveys for analysis, which are susceptible to memory bias and social desirability bias, and commonly conflate the number of items added to a shopping cart with purchase intent. Based on 778 valid purchase sessions from the Retail Rocket public dataset, this paper constructs the core metric of the "add-to-cart-to-view ratio" to identify impulse purchases from the perspective of behavioral conversion efficiency. It also introduces the average dwell time per product to avoid circular reasoning and compares the predictive performance of logistic regression, random forest, and XGBoost models. The results indicate that the "add-to-cart-to-view ratio" is a core predictive indicator of impulse purchases, while the number of times an item is added to the cart alone has no significant impact; the XGBoost model performed best (AUC = 0.7576). This study provides data support for e-commerce platforms to accurately identify impulse buyers and implement targeted marketing strategies.