Evolutionary MNN for Mental Health Analysis of College Students in Social Network Environment

This paper proposes a novel framework, evolutionary intelligent multiple neural networks (EIMNN), for college student mental health analysis and guidance. EIMNN integrates modality-specific neural sub-networks, each tailored to textual content, temporal behavior, and peer interaction signals, within an evolutionary neural architecture search paradigm. Inspired by principles of swarm intelligence, the framework employs a cooperative co-evolution strategy, where multiple neural networks evolve in parallel and exchange structural knowledge to optimize multimodal representations. A co-evolved attention mechanism adaptively fuses outputs from these networks based on individual context. Furthermore, the authors introduce a reinforcement-guided psychological state transition model, which learns to anticipate emotional trajectories and supports proactive mental health guidance. Extensive experiments on a large-scale, multi-modal social media dataset collected from 3,500 university students demonstrate that EIMNN outperforms state-of-the-art baselines in terms of many metrics.