An improved DBSCAN spatiotemporal clustering algorithm is proposed to increase the intimacy analysis accuracy of social relationships hidden in campus wireless network data. First, spatiotemporal trajectories are formed according to the location and time of the WiFi connection by collecting campus wireless network data, and an improved algorithm is used to classify the spatiotemporal trajectories. Then, the characteristic trajectories of the clustering results are extracted, and the LCSS algorithm is employed to measure the similarity of spatiotemporal trajectories. The high similarity between the trajectories indicates the close relationships, and the low similarity reveals those isolated students that need to be further investigated and counseled by teachers. Finally, FinBI is used to visualize the trajectory clustering results. The experimental results show that the improved algorithm can increase the accuracy and effectiveness of the clustering results while providing a reference for solving other similarity problems.