BACKGROUND: Although robots have emerged as a new means of delivering health information, with the advancement of artificial intelligence technology, individuals still face challenges in deciding whether to trust the health information provided by these robots owing to various trust-related factors. OBJECTIVE: This study aimed to investigate the factors associated with the level of trust in health information robots among the general population in China from a socioecological model perspective and identify the central indicators based on network analysis. METHODS: A nationwide survey in China was conducted from June 20, 2023, to August 31, 2023, involving 30,054 participants. The level of trust in health information robots was measured using a self-developed questionnaire. Univariate and multivariate generalized linear model analyses were conducted to investigate the factors associated with the level of trust in health information robots. Network analyses were conducted to examine the network structure of trust levels in health information robots and the associated factors. RESULTS: The results of the multivariate generalized linear model analysis revealed that participants who were diagnosed with chronic diseases; exhibited personality traits of higher agreeableness and openness; had an education level of junior college or higher; reported higher self-rated health status, health literacy, anxiety symptoms, family health, number of house properties, average monthly household income, and perceived social support; and had higher medical insurance coverage showed a positive association with the level of trust in health information robots compared to individuals without these characteristics. However, compared to individuals without these characteristics, being older, having the personality trait of neuroticism, and living in an urban area were negatively associated with the level of trust in health information robots. In addition, using a network approach, central indicators were identified in the network of the level of trust in health information robots and its associated factors, including family health and perceived social support. Finally, agreeableness and educational level appeared upstream of the entire directed acyclic graph, directly influencing the level of trust in health information robots. CONCLUSIONS: Our findings offer a novel perspective on the association between health information robots and trust and contribute to the application and development of artificial intelligence IT. Individuals' acceptance of and adherence to health information may be enhanced if the factors associated with the level of trust in health information robots are considered.