This chapter presents a comprehensive framework for the design and implementation of an intelligent system for computational antitrust, focusing on risk modeling, large model integration, and functional demonstration. It begins with an in-depth analysis of monopolistic behavior types observed in the platform economy, including monopoly agreements, concentrations of undertakings, and abuses of market dominance. Building upon this foundation, a three-tiered risk level system is constructed to classify monopolistic behaviors as low, medium, or high risk, supported by a detailed evaluation of ten key data elements such as market share, entry barriers, network effects, data control, and consumer welfare. The chapter further proposes an intelligent antitrust system architecture that integrates machine learning, knowledge graphs, and large-scale language models to enable real time detection, risk prediction, and regulatory decision support. Through modular design, the system encompasses key functional components—data management, risk identification, visualization, compliance auditing, and intelligent report generation—ensuring adaptability and scalability for complex regulatory scenarios. A computational antitrust large model, fine-tuned using domain-specific corpora and integrated via APIs, provides advanced reasoning and natural language generation capabilities for automated report drafting and cross-departmental coordination. Functional demonstrations highlight the system’s ability to perform statistical analysis, identify discriminatory pricing through Dual Pricing Model Clustering (DPMC), predict monopoly risk trends, and generate intelligent risk assessment reports. The chapter concludes by emphasizing the transformative role of computational antitrust systems in modern governance—enhancing the scientific basis, efficiency, and intelligence of antitrust regulation in the era of the digital economy.