Aware Particle Swarm Optimization Algorithm for Emotion Recognition and New Media Intelligent Interaction

To address the challenges of lacking real-time feedback and insufficient personalization in traditional art therapy, this study proposes a Smart Emotional Healing-Virtual Reality (SEH-VR) model comprising three modules: multimodal emotion recognition, personalized content generation, and adaptive interaction optimization. The study contributions are as follows: (1) A cross-modal fusion algorithm based on the transformer architecture with dynamic allocation of visual-speech attention weights improves emotion recognition accuracy to 0.92 in VR scenarios, reduces processing latency to 150 ms, and optimizes memory usage to 1500 MB. (2) An enhanced Style Generative Adversarial Network 3 (StyleGAN3) model achieves quality and personalization scores of 0.85 and 0.80, respectively, for generating art therapy content, while shortening generation time to 45 seconds. (3) The Context-Aware Particle Swarm Optimization (CA-PSO) algorithm optimizes system response time to 120 ms, stabilizes frame rate at 50 fps, and reduces system load to 55%.