## 1 Introduction  
The global population aged over 60 is expected to triple, reaching 2 billion by 2050 [1]. By 2025-2026, an estimated 2 million individuals aged over 50 are expected to encounter social isolation and loneliness, signifying a 49% increase within a decade [2]. People have used life stories to break social isolation and alleviate loneliness among older adults [3]. However, life stories are often obtained through frequent brief interviews or digital storytelling and are seldom recounted in their entirety, resulting in fuzziness, fragmentation, and redundancy [4]. Moreover, the disordered aggregation of life stories results in ambiguous relationships and indistinct themes. This complexity and uncertainty make it difficult for users to perform information mining and make analytical decisions based on life stories [5]. Extracting crucial events from life stories and organizing the events into a hierarchical tree-like structure presents an effective approach to the aforementioned issues. Currently, research on structured event organization predominantly focuses on the news and social media. [PERSON] et al. employed Twitter data to construct a storyline of personal events by discerning important occurrences based on user mentions [6]. They prioritized events mentionedonce as significant and overlooked frequently discussed events. Yet, this approach is not suitable for older life story organizations, as events that older people often discuss are important in establishing links in elderly care services. [PERSON] et al. designed an event tree-building algorithm for identifying events through user network communities and modeling their evolution based on temporal proximity and semantic context [7]. However, it did not address the issues of event repetition and fragmentation. [PERSON] et al. introduced a dual-layer graph clustering technique to analyze event streams, segmenting documents into topic sets for event tree creation [8]. Nonetheless, their method assessed event relevance solely on individual event text features, overlooking the interconnections between events. The uniqueness of older adults' life narratives, distinct from news or social media data, compounds three challenges in structuring them. Firstly, older adults often recall specific life experiences in varying ways, leading to fragmented and repetitive narratives. This poses challenges for caregivers, who face the pressure of reading and understanding these stories. Thus, in structuring life narratives, it is essential to identify and merge fragmented life stories depicting the same event. Secondly, older adults narrate their life stories without adhering to clear topics, encompassing various topics within their daily conversations. Hence, we need to cluster related events revolving around the same topics. Finally, the representation of event features in life stories needs to consider multidimensional information. Elements such as characters, time, and location within events, along with potential relationships between events, all influence the representation of event features. Presently, deep learning models predominantly focus on enhancing event features based on textual content and trigger words, paying relatively little attention to the information regarding event elements and the potential connections between events. We compress the text features of life stories into two-dimensional space by linear embedding to visualize the problem. Figure 1a illustrates the original feature distribution of a person's life stories, in which each point represents a life story and different story topics are distinguished with different colors. As shown in Figure 1a, the related life stories are scattered. Our goal is shown in Figure 1b, where relevant event features become more similar, while irrelevant event features become more distinct. To address the above questions, this paper proposes a novel structured life narrative construction method, LSH-GEFR, which builds life story hierarchies with Graph-Enhanced Event Feature Refinement.