This paper provides a detailed overview of the NLPCC 2025 Shared Task 8, which focused on personalized emotional support conversation. Addressing the dual challenges of acquiring high-quality, nuanced datasets for emotional support and the limitations of generic Large Language Model (LLM) responses, this task aims to encourage the development of systems capable of delivering empathetic and contextually relevant support by taking individual user characteristics into account. The central objective is to train models on a newly created dataset, UniConv, to generate supportive dialogue that acknowledges and responds to users’ unique profiles and situations. This paper outlines the motivation behind the task, the creation and specific details of the UniConv dataset, the evaluation framework employed, the approaches taken by the participating teams, and a summary of the achieved results. Looking ahead, we anticipate that continued exploration and refinement of personalized modeling approaches will pave the way for the development of next-generation emotional support systems that are more attuned to user needs and more human-centered. A total of 3 teams participated in the task, submitting 12 system results.