The growth of air traffic and operational complexity requires proactive digital tools for civil aviation safety management. Official incident investigation reports contain validated safety knowledge, yet their narrative form limits direct use in automated analytics and decision support. This paper proposes an applied information technology framework for classification and risk prioritization of civil aviation incidents based on officially investigated reports from the Republic of Uzbekistan for 2025. The research objective is to transform unstructured investigation narratives into structured analytical inputs suitable for algorithmic processing within a Safety Management System. A rule-based text mining methodology is developed to extract operational context, system involvement, human and external factors, and formal causal conclusions. Incidents are classified into technical, human, external, and combined categories, and a qualitative risk relevance scoring model is introduced considering system criticality and flight-phase sensitivity. The results demonstrate that actionable safety intelligence can be derived from limited but high-quality national datasets and integrated into lightweight decision-support modules. The proposed approach provides interpretable analytics aligned with regulatory practice and supports prioritization of mitigation measures. The framework has practical value for SMS monitoring and contributes to explainable applied IT solutions in safety-critical aviation environments.