Cross-tabulation of study participants by gender.

ObjectiveDali is a city rich in tourism resources and cultural heritage, where residents’ subjective well-being (SWB) varies in response to the dynamics of local tourism culture. Few studies have examined the distribution of SWB levels and their influencing factors in areas where modern tourism economies and traditional cultures coexist. The study aims to explore the relationship between multiple variables and SWB, and rank the importance of key well-being factors.MethodsThis study employed a convenience sampling method to survey permanent residents of Dali City, resulting in a final dataset of 483 valid samples. Our study selected a wide range of predictors, including sociodemographic characteristics, leisure activities, social class identification, and preferences in socialization interaction patterns. Eight common ML algorithms were utilized to construct prediction models. The model’s performance was evaluated using the area under the curve (AUC) metric. Generalized additive models (GAMs) were used in sensitivity analyses to assess potential nonlinear relationships between predictors and SWB.ResultsThe probability of high SWB in Dali City was 48.9%. RF demonstrated the highest predictive accuracy (AUC = 0.82). By ranking the importance of variables in the best model RF, we obtain the top five predictors of SWB as: frequency of health issues affecting daily activities, family economic status, age, income, and weekly family face-to-face communication. GAMs explained 55.2% of the variance in SWB (R2 = 0.552, N = 483). Fewer health issues affecting daily life were strongly associated with higher SWB (B = 4.83–6.39, p p p p ConclusionsThis study shifts the focus from economic outcomes to residents’ SWB in a culturally diverse tourism setting. Using machine learning and GAMs, health issues emerged as the strongest predictors of SWB. Findings support health-oriented tourism strategies and highlight the need to integrate socio-cultural factors into sustainable tourism planning.研究背景：大理市拥有丰富的旅游资源与文化遗产，当地居民的主观幸福感（subjective well-being, SWB）会随当地旅游文化的动态变化而产生差异。目前鲜有研究探讨现代旅游经济与传统文化共生地区的主观幸福感水平分布及其影响因素。本研究旨在探究多变量与主观幸福感之间的关联，并对关键幸福感影响因素的重要性进行排序。 方法 本研究采用方便抽样法（convenience sampling method）对大理市常住居民开展调查，最终获得483份有效样本。本研究选取了涵盖社会人口学特征（sociodemographic characteristics）、休闲活动、社会阶层认同（social class identification）以及社交互动模式（socialization interaction patterns）偏好在内的多维度预测变量。研究使用8种常见机器学习算法（machine learning algorithms）构建预测模型，并以受试者工作特征曲线下面积（area under the curve, AUC）作为模型性能评估指标。此外，本研究借助广义可加模型（Generalized Additive Models, GAMs）开展敏感性分析，以评估预测变量与主观幸福感之间潜在的非线性关联。 结果 大理市居民高主观幸福感的占比为48.9%。随机森林（Random Forest, RF）模型展现出最高的预测精度（AUC=0.82）。通过对最优模型RF中的变量进行重要性排序，得到主观幸福感的前五大预测因子依次为：影响日常活动的健康问题发生频率、家庭经济状况、年龄、收入以及每周家庭面对面交流时长。广义可加模型可解释主观幸福感55.2%的方差变异（R²=0.552, N=483）。影响日常生活的健康问题越少，主观幸福感越高（B=4.83–6.39, p p p p）。 结论 本研究将研究视角从经济成果转向文化多元旅游场景下的居民主观幸福感。通过结合机器学习与广义可加模型，研究发现健康问题是主观幸福感最强的预测因子。研究结果支持以健康为导向的旅游策略，并强调需将社会文化因素纳入可持续旅游规划之中。