The rapid proliferation of decentralized digital communication platforms and the ubiquitous implementation of end-to-end encryption have precipitated a critical crisis in digital forensics, significantly exacerbating the complexities of identifying and mitigating online child exploitation (OCE). Predatory actors increasingly deploy highly sophisticated, adversarial linguistic tactics—such as localized transient slang, leetspeak obfuscation, and emoji-driven euphemisms—that systematically bypass traditional deterministic content moderation subsystems and rigid keyword heuristics. This research presents a unified, modular deep learning architecture engineered explicitly for the automated, low-latency recognition of predatory grooming trajectories within unstructured text streams. The proposed framework initiates with the Semantic-Aware Transformer Ensemble for Predatory Intent Recognition (SATE-PIR), a parallel architecture synthesizing RoBERTa, DistilBERT, and ALBERT backbones beneath a neural meta-classifier. SATE-PIR mathematically maps the deep contextual semantic intent of localized conversational exchanges, identifying covert coercion cues independent of explicit vocabulary. To resolve the quadratic computational constraints of transformers in modeling protracted, multi-session interactions, the system subsequently introduces the Real-Time Dynamic Sequence Long Short-Term Memory (RT-DSL) framework. RT-DSL implements a novel continuous temporal attention decay parameter, algorithmically scaling historical cell memory based on real-time asynchronous clock-time differentials to prevent context fragmentation during long-term anomaly tracking. Evaluated against the consolidated PAN-CLEF and Perverted Justice forensic corpora, SATE-PIR achieves a peak F1-score of 97.3%, while RT-DSL maintains robust longitudinal sequence classification at an operational inference latency of 4.9 milliseconds per message. Ultimately, this architecture establishes a resilient, privacy-preserving standard for edge-deployable child protection intelligence systems.

Keywords---Online Child Exploitation; Deep Learning; Natural Language Processing; Transformer Ensembles; Long Short-Term Memory; Temporal Anomaly Detection; Digital Forensics; Predatory Intent Recognition.