The Outer Burial Pits of the Han Dynasty Yang Mausoleum is the first fully enclosed site museum in China. The Internet of things sensor installed in the pavilion has accumulated more than 7,000,000 heterogeneous data. Traditional algorithms, such as temperature prediction model, only use statistical value to predict the trend of temperature change, so the data utilization rate is insufficient. In addition, the accuracy of prediction model is also relatively low. The extreme learning machine is a single layer feedback neural network learning method. Using extreme learning machine to analyze and model data is an effective prediction algorithm for preventive protection. However, due to the mismatch between solar calendar characteristics and temperature changes, the accuracy of the prediction model is unsatisfactory. To solve the above problem, a temperature prediction model based on lunar calendar characteristics is proposed in this paper. A large number of measured data show that the model can more accurately predict the future temperature variation trend and detailed characteristics. Its performance is better than that of the model based on solar calendar characteristics. In this paper, a new prediction model is proposed. The advantages of lunar calendar in natural data recording and processing are preliminarily verified, which provides reference for the label selection of follow-up monitoring data.