Recently, information and digital technology are widely used in thousands of industries, leading to intelligent transformation, traditional methods, which lacks intelligent instrument. The safety of college students has attracted widespread attention from all walks of life, while campus safety management still adopts manual and traditional methods, which lacks intelligent instrument and big data resources and technologies are not fully utilized. In this paper, we propose a system of campus safety management based on telecom big data and data fusion architecture, providing solutions for intelligent campus management. In addition, a prediction algorithm of student behavior intent considering time spans has been proposed, proving the advantages in accuracy metrics and F1-score compared with traditional prediction algorithms.

With the disappearance of the demographic dividend and the saturation of the public telecom market, telecom operators need new development strategies urgently. New services formed by the convergence of mobile network services and broadband network services (referred to as fixed-mobile convergence services) have become an important strategy. Through business innovation, telecom operators can bundle mobile services with broadband services, which can enhance user stickiness and increase business revenue. Based on the joint analysis of mobile network data and broadband network data, this paper proposes a rule-based and model-based integrated method for mining potential fixed-mobile convergence target users. After applying this method to the real market for single mobile contract user transferring to convergent contract, results show that the proposed method for exploiting potential target users can increase the conversion rate of convergence users.