OBJECTIVE: Although smart senior care services offer numerous benefits, they have not yet gained widespread acceptance among the general populace, particularly seniors. Numerous related issues have surfaced, with the structural imbalance between supply and demand being most prominent. Currently, there is a lack of research distinguishing between the various categories of demand for smart ageing services and the associated behaviors of older individuals. In this study, we aimed to identify the types of demand and utilization of smart senior care services among Chinese older adults to understand their diverse characteristics and the factors that facilitate certain behaviors. We also analyzed the imbalance between supply and demand for smart senior care services and explored the factors influencing it, thereby providing a reference for optimizing smart senior services. METHODS: We conducted a cross-sectional study from January to March 2023 and analyzed 1037 valid questionnaires. Three types of smart senior care services were investigated: intelligent information, intelligent consultation, and intelligent monitoring. We identified the demand, utilization, and supply of these services among older individuals. Latent class analysis (LCA) was used to differentiate the heterogeneity of older adults in terms of service demand and utilization. Factors influencing service preferences were analyzed using binary logistic regression based on Andersen's behavioral model. RESULTS: Based on the LCA findings, service demand, and utilization were divided into two categories: positive demand (desire to use the services) or negative demand, and taking action or negative action to use the services. The persons with high demand but low utilization comprised the largest number of older people in this study (69.35%). The results indicated that the number of children (odds ratio (OR) = 1.491), community-provided smart devices (OR = 1.700), number of chronic diseases (OR = 1.218), and self-care capacity (OR = 0.214) are associated with positive demand. Meanwhile, pre-retirement employment, income sources, community device provided, community promotion, region, and self-care ability were significant predictors (p < 0.05) of taking action to use the services. In terms of community supply outcome, income situation had a significant effect on intelligent information services. Income sources were associated with intelligent information and intelligent monitoring services. Pre-retirement employment and housing type variables showed effect on IC services. Community promotion and self-care ability were associated with all three types of service supply (p < 0.05). CONCLUSION: Older adults expressed a strong demand for smart ageing services; however, difficulties using smart technology remain a serious problem. Further investigation of how family support contributes to the perception and use of care services for older people is needed. Specific policies, such as financial assistance, should be established to support service use. Communities should expand their support and promotion of smart ageing services, focusing on enhancing digital health literacy among seniors to facilitate product utilization. Furthermore, personalized recommendations and applications tailored to the physical conditions of older adults are essential.