With the rapid popularization of smartphones and tablets, there are thousands of applications based on mobile social networks. The big data from these networks provide a huge potential to shed light on the mobility patterns of users. These big data enable a deeper understanding of users' preferences and behaviors and will help us mine users' friendship in both physical and digital worlds. In this paper, we firstly divide user mobility patterns into different categories to portray the characteristics of user encounter more precisely. Then, with combining proximity data from bluetooth devices and location data from cellular towers, we introduce a set of spatial-temporal features, including the encounter entropy, which measures the probability of encounters between different mobile users. Using these spatial-temporal features, we provide a novel model to infer user friendship by analyzing the social context of users and their encounters. To address the class imbalance problem in the dataset and improve the prediction accuracy of friendship, we employ the sampling method and evaluate our model with three different classifiers. The experimental results show that our encounter entropy feature has a striking effect to infer user friendship, and our model based on these spatial-temporal features can achieve pretty good accuracy in predicting friendship over real human mobility traces without privacy-sensitive information disclosure.