Social network analysis (SNA) is a popular research topic in the field of data mining, and the quality and the scale of networks are extremely important for the research. But most previous studies are conducted on large online social networks or small real social networks. Online social networks are only the approximation of real social networks, and in general they have different properties. Some research conducts on real social networks which are constructed from the survey of quite small population. Social network study expects large real social network data. Transaction logs generated by modern software systems make it possible to construct social networks from large real social data. This paper conducts a case study of extracting student social network (SSN) from school card system transaction logs to explore how to extract social network from transaction logs. Firstly, we build a relation extracting model based on co-occurrence. Then, we define probability coefficient for edge based on the weight of edge and Jaccard coefficient, filtering noisy edges from the network. We conduct our method on real transaction logs data, and the experiments show that our method can generate social networks with high precision. The topology of the network shows that this social network has small-world network features and a scale-free degree distribution.