This paper focuses on the precise identification of potential cloud computer users utilizing telecom big data. By integrating multi-source heterogeneous data and leveraging key technical capabilities, including raw bitstream network data parsing and the construction of an innovative O-domain hierarchical tagging architecture, we develop a multidimensional user behavior profiling model. This model enables accurate identification of cloud computer usage patterns for both enterprise and individual users. Key innovations include: (1) a novel O-domain tagging framework that substantially enhances scenario classification accuracy; (2) an attentivestacking fusion model that dynamically prioritizes telecomspecific behavioral features to optimize prediction performance; and (3) a validated analysis plan demonstrating superior conversion outcomes and reduced operational costs in real-world deployments. The proposed attentive-stacking fusion model, trained on behavioral characteristics unique to telecom scenarios, significantly enhances the prediction accuracy of potential cloud computer user groups. Comparative experiments confirm that the analysis plan efficiently identifies high-conversion-potential cloud computer user segments, effectively addressing the challenge of high customer acquisition costs inherent in traditional marketing methods. This research establishes a novel pathway for telecom operators to leverage big data for targeted user mining, marketing expenditure reduction, and conversion efficiency improvement.

In mobile communication networks, host classification plays a critical role in constructing user profiles and ensuring network security. Traditional approaches, which rely on rule-based matching and shallow feature engineering, face significant limitations in coping with the high-frequency dynamic variations of hostnames and the labor-intensive maintenance of manual rules. To address these challenges, this paper proposes a novel frequency-aware hybrid-granularity tokenization method, specifically designed to capture both the semantic structure and statistical patterns of hostnames. By leveraging semi-supervised learning on large-scale host sequence data collected from real-world network environments, the proposed method enables effective service classification through vectorized host representations. This work not only offers an efficient and scalable solution for host analysis in personalized recommendation systems and mobile network security but also provides valuable insights into the design of pretrained tokenizers tailored for dynamic data scenarios.

Due to the implementation of national security-related laws and regulations, data privacy protection and ownership issues have attracted much attention, meanwhile, the rise of technologies such as 5G, IOT, big data, and edge computing has promoted the digital transformation of data as a factor of production to empower social governance. At present, traditional machine learning still uses the method of data-centered large models for training and reasoning. This method brings about data fragmentation and island distribution and other problems, which have become the key problems restricting the popularization of Artificial Intelligence (AI) technology applications. In this paper, we explore a federated learning model for user complaint warning algorithm based on big data and other enterprise side data. The experimental result also shows that the prediction accuracy of the federated learning model and the traditional logistic regression model are within an acceptable range on the premise of ensuring user privacy and data security.

With AI and big data technologies, telecom operators are looking to change the traditional O&M model from reactive problem handling to proactive prevention and prediction. This paper proposes a model framework trained on multiple data sources for the 5G wireless network to support proactive O&M tasks based on complaint prediction. By grouping user complaints into base station complaint prediction, the model enhanced precision scores while maintaining high recall scores. The model has been integrated into the operator’s work order system to support intelligent operational optimization workflow.

User address localization technologies have far-reaching implications for operators and governments. These technologies serve to optimize the planning and operation of communication networks and enhance the quality of network services, thereby providing powerful support for the provision of social public services, urban planning, and emergency management. The existing user address localization methods used by operators mainly rely on base stations locations and user-reported GPS. However, when the coverage area of a base station is too large, the localization accuracy will be poor even if triangulation techniques are used. Moreover, since the user-reported GPS is triggered by specific network events, its time continuity and volume are unstable. To address these limitations, this paper provides a user address localization method based on data fusion of signaling data from core network, measurement report (MR) data from radio access network, and operational data from carriers’ business support system (BSS). The feature engineering is performed from three dimensions, including base station level analysis, GPS level analysis, and address level analysis, to construct a building-level user address localization model. During data fusion, the signaling data compensates the weak time continuity of MR data, and the MR data compensates the poor localization accuracy of signaling data. In addition, through fusion with BSS data, it is able to convert users’ GPS positions into building-level address locations, thereby further improving the practicality and accuracy of the model..

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.

Psychological health of students has become a widespread social problem, while the management and assessment of college students' psychological health is still stay in passive and manual mode based on the traditional method. In this paper, we design a novel architecture for the prediction of college students' psychological health based on Multi-Source big data including Operation Support System big data, educational data and psychological health questionnaire data. Then we propose the Optimized Decision Tree using Multiple-Target Particle Swarm Optimization (DT-MTPSO) algorithm. Experiment shows that the proposed algorithm can solve the Multiple-Target problems effectively and has better performance in F1-score than traditional Decision Tree. In addition, the result of the features selection of DT-MTPSO for different targets shows the relationship between the psychological health level and behavioural characteristics of students for different evaluation indicators, providing guidance to the school managers and educational psychologist.

Data expansion is one of the commonly used steps in big data analysis applications. This paper proposes a data expansion method, which is based on operator data and considers multiple scenarios, multiple operating systems, and multiple operators in the target area. Factors such as the proportion of share and the difference in the proportion of users in the consumption power portrait are comprehensively expanded to obtain the full amount of user data of each target group in the target area. This method can be prepared to reflect changes in user data in time, and is applied to industries such as scene-based marketing and business planning.

The economic expenditure of mobile network operators includes two parts, namely CAPEX and OPEX. CAPEX mainly includes the huge amount of capital invested in network infrastructure construction, while operating expenditure mainly includes expenditure for daily operation and maintenance. In order to achieve continuous coverage of wireless network, CAPEX needed for base station procurement is indispensable. Operators need to adopt more intelligent and scaled means to optimize the maintenance process of wireless network so as to better achieve the goal of cost reduction and efficiency increase. In this paper, a scheme of cell boundary prediction and base station location information verification based on machine learning is proposed, which innovatively introduces the machine learning algorithm into network optimization analysis and improve the verification efficiency and reduce the input of manpower.