[Objective] This research characterizes users’ browsing patterns, aiming to predict their purchasing decisions on mobile shopping applications. [Methods] First, we mapped the request parameters of the logs with users’ information behavior types. Then, we used logistic binary regression and C&R decision tree techniques to establish models to predict the buying decisions. The data set included 3,923,429 lines of server logs generated by 290 heavy users of a popular mobile shopping app in March 2015. [Results] We found that the frequency of users’ browsing behaviors was stable during the weekdays and reached its peak every night before bedtime. Users paid much attention to product details and those with deeper browsing behaviors are more likely to read introduction to the shop and share related information. The number of views was in line with the power-law distribution and 90% of the merchandise was checked less than 16 times. We also found that goods viewed by 9 times and placed in the carts were most likely to be bought. There was a positive correlation between the purchases of goods and the numbers of views or sharing of the item and the shop. The C&R decision tree model’s prediction accuracy was slightly higher than that of the Logistic binary regression model. However, the former’s variable types were far less than the latter. [Limitations] Logs cannot fully reflect all users’ behaviors, which lead to some ambiguity of our analysis. The conclusion might not tell the whole story since the logs were generated by heavy users in one month. [Conclusions] The pattern of user browsing and buying behaviors could be used to enhance their experience of the mobile shopping applications. Logistic binary regression might better predict users’ buying decisions than the C&R decision trees model.