Photonics-enhanced intelligent decision framework for cognitive and behavioral learning analytics

The rapid convergence of optical sensing, photonic computing, and artificial intelligence is reshaping the landscape of intelligent information systems. However, existing data-driven frameworks struggle to achieve real-time multimodal perception and energy-efficient inference under large-scale, high-dimensional conditions. This paper presents a Photonics-Enhanced Intelligent Decision Framework that integrates optical behavioral sensing, unsupervised representation learning, and photonic neural computation into a unified architecture for adaptive decision optimization. The framework begins with multimodal data acquisition from optical and digital sources, followed by clustering-based feature extraction to construct latent representations of system states. A photonic-aware neural network, implemented via optical matrix multiplication, enables sub-nanosecond inference with significantly reduced energy consumption, while a symbolic rule-enhanced integration layer ensures logical consistency and interpretability of decisions. Experimental results demonstrate that the proposed method achieves superior inference latency, throughput, and energy efficiency compared with conventional GPU-based models, without sacrificing decision accuracy or stability. This synergy between optical perception and photonic computation establishes a new paradigm for high-speed, low-power, and explainable intelligent decision systems applicable to complex cyber–physical environments.