The process of urbanization is formed by regular movements of human beings. It yields different functional zones in a city, such as residential zone and commercial zone. Consequently, there exists a close connection between the human mobility pattern and the city’s zones. However, it is not easy to collect large-scale society-wide data that can precisely capture the underlying relations between the individual’s movement and the regional functions. Hence, our knowledge for understanding the basic patterns of human mobility is still limited. In order to discover the functions of different regions in a city, we propose an affinity based method in this paper. The affinity is a recently introduced metric for measuring the correlation of two connecting node in a complex network. The proposed model groups different functional zones by measuring user’s arrival/departure distribution via relative entropy. In addition to this, we also identify the intensity of each functional zone by taking kernel density estimation(KDE) method. In the end, some experiments are conducted to evaluate our method with a large-scale real-life dataset, which consists of 3 million cellphone users’ records from a period of one month. Our findings on the interaction between the mobility pattern and the regional functions can capture the city dynamics efficiently and provide a valuable reference for urban planners. The process of urbanization is formed by regular movements of human beings. Such, there exists a connection zone between a city, such as residential zone and commercial zone. is not easy to collect large-scale society-wide data that can precisely capture the underlying relations between the individual’s movement and the regional functions. Therefore, our knowledge for understanding the basic patterns of human mobility is still limited. In order to discover the functions of different regions in a city, we propose an affinity based method in this paper. The affinity is a recently introduced metric for measuring the correlation of two connecting node in a complex network. The proposed model groups different functional zones by measuring user’s arrival / departure In addition to this, we also identify the intensity of each functional zone by taking kern In the end, some experiments are conducted to evaluate our method with a large-scale real-life dataset, which consists of 3 million cellphone users’ records from a period of one month. Our findings on the interaction between the mobility pattern and the regional functions can capture the city dynamics efficiently and provide a valuable reference for urban planners.