<![CDATA[The contemporary defense environment faces rapidly evolving threats, vast heterogeneous data, and linguistic diversity, creating significant challenges for timely and accurate intelligence analysis. This study aims to develop an integrated big data analytics framework that combines open-source intelligence, social media monitoring, and satellite imagery into a unified temporal knowledge graph to support multilingual, cross-modal threat assessment. The proposed methodology incorporates five key phases: multi-source data collection and preprocessing, multilingual transformer-based natural language processing for entity, relation, and event extraction, temporal knowledge graph construction, machine learning-driven analytical modeling for threat prediction and risk assessment, and comprehensive evaluation using both system performance and intelligence value metrics. Experimental results demonstrate that the framework achieves superior entity recognition (F1-score 0.882) and relation extraction (F1-score 0.869), reduces processing latency by 92.6% compared to baseline systems, and integrates 6.3 million entities across 15 languages. Multi-source data fusion improves assessment accuracy by 18.4%, enabling near real-time situational awareness and enhanced strategic decision-making. The system’s explainable reasoning and temporal modeling capabilities provide transparent, actionable intelligence for defense planners, addressing limitations of traditional single-modality and monolingual systems. These findings indicate that integrating multilingual NLP, cross-modal fusion, and temporal knowledge representation significantly enhances operational readiness and early warning capabilities, offering a practical framework adaptable to national and regional security contexts.]]>
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