Due to the fewer uncertainty samples and information-lacking problems in the decision-making center of the Internet of Things (IoT) for green buildings, the optimized model was selected as the preferred method in settlement prediction. In this paper, we proposed the adaptive adjustment strategies of the application layer in IoT for green buildings based on grey incidence analysis and artificial neural networks (ANNs). An additional layer H fuzzy propagation natural network algorithm was introduced to collect sensing layer data of IoT and adaptively adjust decisions. The energy-saving control of the building needs to be adjusted continuously; therefore, we have taken a grey incidence evaluation to obtain adjustment of the parameters. At the same time, the actual Heating Ventilation Air Conditioning subsystem is often in the grey state above, and the current control system of its system is missing the corresponding adjustment scheme. The introduction of the data evaluation in the data center for adaptive adjustment of input data is an effective solution. The real-time running result shows that the proposed solution reduces energy consumption by over 30% compared to the state-of-the-art approaches while having on average 10% fewer expired measurements. The strategies have a significant impact on energy savings for green buildings.