HCEA: A Multi-Agent Framework for Sustainable Human-Centered Entrepreneurship Based on a Large Language Model

Human-centered entrepreneurship considers employee well-being and uses the Sustainable Development Goals as its fundamental pillars. However, existing research predominantly focuses on institutional interventions and fails to provide integrated intelligent solutions for tackling human–machine collaboration issues in the context of digital transformation. Large language models (LLMs) offer potential for affective computing and personalized support, but face critical gaps in ethical governance, privacy protection, and real-time risk intervention in sensitive entrepreneurial contexts. Our proposed Human-Centered Entrepreneurial Intelligent Agent (HCEA) framework achieves the unified optimization of task utility, empathetic expression, and ethical security by integrating a large language model core fine-tuned via a multi-objective hybrid loss function and a cluster of task-specialized intelligent agents. HCEA integrates retrieval-enhanced generation to ensure suggestion accuracy, a hierarchical data governance system for sensitivity-based privacy protection, and an independent risk detection module for real-time intervention and referral. We build the framework by constructing a hybrid entrepreneurial dataset, design the multi-agent architecture of decision support, emotion understanding and ethical risk tracking, and empirically evaluate both comparisons and ablation experiments. The results demonstrate that HCEA outperforms five baseline models across six key metrics, including entrepreneurship guidance relevance, emotion recognition, and high-risk recall. This study contributes to the intersection of digital transformation and sustainable entrepreneurship by providing a technically feasible, ethically grounded intelligent framework that empowers enterprises to reconcile efficiency with human-centric values, advancing SDG 8 (decent work and economic growth) and SDG 9 (industry, innovation, and infrastructure).