To address the digital transformation needs of heritage interpretation services in smart tourism, in this study, we developed and evaluated an IoT sensor-enabled heritage interpretation system integrating environmental sensing, proximity detection, and mobile interactive interfaces within a unified IoT architecture. The system includes a sensing layer (environmental sensors, Bluetooth beacons, and mobile sensing modules), a network transmission layer, and an application layer, where real-time sensor data are transmitted through IoT middleware and converted into adaptive servicescape cues for personalized content delivery. This framework demonstrates how heterogeneous sensor signals—such as proximity detection, node identification, and spatial positioning—can be integrated to support context-aware interpretation services in heritage environments. Within this sensor-integrated framework, we validated a serial mechanism linking an IoT-enabled heritage servicescape (I-SC) and interpretation service quality (ISQ) to overall service quality (OSQ), positive affect (PA), perceived value (PV), and behavioral intention (BI) using a servicescape stimulus–organism–response (S–O–R) structural modeling approach. This approach provides a quantitative framework for evaluating how sensor-generated environmental signals affect human perception and behavioral outcomes, extending sensor-enabled systems beyond technical deployment toward human-centered effectiveness evaluation. Visitor perceptions were collected via questionnaire survey after interacting with the sensor-enabled system, followed by item analysis, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM). The instrument showed strong reliability (Cronbach’s α = 0.908). Sampling adequacy was excellent (Kaiser–Meyer–Olkin = 0.954; Bartlett’s χ2 = 17816.455, df = 1326, p < 0.001). EFA extracted 13 factors explaining 67.5% of the total variance. CFA indicated good model fit (χ2/df = 0.942, Comparative Fit Index = 1.000, RMSEA = 0.000, SRMR = 0.023), with standardized loadings ranging from 0.741 to 0.951, the composite reliability (CR) of 0.819–0.967, and the extracted average variance of 0.530–0.880, supporting convergent and discriminant validity. SEM supported all hypothesized paths: I-SC→PA (β = 0.539), I-SC→OSQ (β = 0.280), ISQ→OSQ (β = 0.465), OSQ→PV (β = 0.539), PA→PV (β = 0.273), PV→BI (β = 0.531), and PA→BI (β = 0.197). PV emerged as the most proximal driver of revisit and recommendation intentions. The results showed that sensor-integrated servicescape cues and real-time IoT data adaptation significantly enhance perceived service quality and affective responses. IoT sensor-enabled interpretation systems should thus prioritize intelligent sensing integration and dynamic content adjustment to increase PV, OSQ, PA, and BI. This study has some limitations. The sensing infrastructure mainly focused on location and interaction detection, and the empirical validation was conducted at a single heritage site, which may limit generalizability. Future research can incorporate additional sensor types and test the system across diverse cultural heritage environments.