Exploring the optimization mechanism of non-invasive temperature and humidity monitoring technology for bee colony health management during the overwintering period of Chinese honeybees (Apis cerana cerana), and constructing a precise and low-cost Internet of Things (IoT) monitoring system to improve the survival rate and feeding management efficiency of bee colonies in autumn and winter. Developing a honeycomb temperature and humidity monitoring system (HTHS) based on the IoT architecture, which integrates the main control module, ESP8266 WIFI chip, and DHT11 sensor. By deploying honeycomb center, edge, and environmental monitoring nodes, data collection was conducted every 5 minutes. Combined with Z-score outlier removal, Pearson correlation analysis in SPSS 26.0 statistical software, and temperature gradient curve fitting using Matlab 7.0 software, continuous monitoring data during the wintering preparation period and wintering period were compared. HTHS monitoring data shows that bee colonies maintain stable temperature and humidity in the central area at (33.3±1.2) ℃ and 53.6%±2.0% through aggregation behavior, while the edge areas are significantly affected by environmental infiltration. The above monitoring data is highly consistent with the results of manual unboxing inspection. HTHS monitoring data further shows that during the overwintering stage, bee colonies have outstanding ability to regulate the temperature at the center of the hive. When the ambient temperature is 0～19.6 ℃, the nest temperature can be maintained at (25.4±1.9) ℃. HTHS provides IoT technology support for extreme climate warning and bee colony health management through non-invasive continuous monitoring and precise analysis of honeycomb temperature and humidity spatiotemporal dynamics. The low-cost and low interference characteristics of this system help promote the transformation of traditional beekeeping industry towards data-driven “precision beekeeping” mode.