In wireless cellular system, due to the convergence of user behavior, there usually exists some typical scenarios which exhibit different traffic patterns, e.g. stadium, campus and central business district (CBD). Accurate traffic scenario recognition and analysis will lead to more efficient resource management and better QoS (quality-of-service) provision. In this paper, with the idea of social network analysis, a base station social network (BSSN) based method is proposed to recognize and analyze the typical traffic scenarios in wireless cellular system. Firstly, we construct a BSSN to visualize the hidden relationship between base stations (BSs) by using previously measured spatial-temporal wireless traffic data. Then, a modularity optimization based method is used to discover community structure in BSSN. Analytical results show that each community in BSSN corresponds to a typical wireless scenario with a unique traffic pattern in cellular system. Experimental results illustrate that our proposed BSSN based method is general enough and achieves satisfactory performance in traffic scenario recognition and analysis.