Nowadays, face recognition technology keeps advancing and smart face capture cameras are widely and intensively deployed. Aiming at the application scenario of gang criminal case detection, this study conducts in-depth data mining and explored the co-occurrence relationship between pedestrians. Other gang members are locked through the realistic social network of suspects. After experimental comparison and demonstration, this study uses the Chinese Whispers clustering algorithm to identify pedestrian nodes and Faiss to accelerate the construction of adjacent edges. In this way, the initialization of the graph is sped up, which improved the clustering efficiency. On this basis, the co-occurrence frequency and the confidence in the Apriori algorithm are used to mine the co-occurrence relationship between pedestrians, resulting in the graph of the co-occurrence relationship.