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In this study, we propose a novel system, TACTIC-GRAPHS, which integrates complex mathematical mechanisms and graph neural reasoning structures for semantic understanding and threat recognition in tactical video under high noise and weak structure conditions, breaking through the traditional empirical AI paradigm by innovatively introducing graph spectral theory embedding, temporal causal edge modelling and multivariate discriminative path inference mechanism, and establishes a multimodal graph inference model with structural interpretability and causal loop closure capability. An intelligent keyframe hierarchical extraction algorithm (ILKE-TCG) is designed to extract semantically-driven keyframe nodes from video, fusing image structure, voice rhythms and action paths to construct a heterogeneous temporal graph. Through the graph attention mechanism and Laplace spectral space mapping technique, the system achieves cross-modal node weight estimation and causal signal deconstruction in spectral space. Experiments on the TACTIC-AVS and TACTIC-Voice datasets show that the model achieves an accuracy of 89.3% in multimodal temporal alignment recognition, with a complete threat causal chain recognition rate of more than 85%, and the node inference latency is controlled within the range of ±150 ms, which is significantly better than existing CNN/Transformer fusion methods. In particular, the introduction of spectral graph theory enhances the structural verifiability and variable distinguishability of causal paths, and pushes the TACTIC system from shallow fusion to deep structural modelling paradigm.TACTIC-GRAPHS not only provides tactical mission type discrimination and threat intensity scoring, but also achieves a number of breakthroughs in the areas of high-dimensional graph structural modelling, complex mathematical path recognition, and cross-modal variable embedding. breakthroughs. This research provides theoretical support and modeling paradigm for the deployment of structural intelligence systems in intelligent security, battlefield sensing, law enforcement identification and national surveillance systems, and represents the cutting-edge direction of multimodal AI causal modelling and a new level of complex reasoning systems.