Abstract: In order to solve the problems of subjectivity, difficulty in selecting super parameters, poor interpretability and ineffective use of temporal information among samples on the existing methods of gas concentration prediction and gas safety state classification, this paper proposes a novel gas concentration prediction and safety state classification method based on the mixture of Gaussian processes. In fact, Gaussian process model is a classic method to solve nonlinear regression problems in machine learning. It can effectively get the correlations between temporal data and thus is often used in time series modeling and prediction. However, a single Gaussian process has certain limitations, and cannot model the data generated from a non-stationary source. The Mixture of Gaussian processes (MGP) can enhance the model capacity, and fit the data with complex structure. We try to divide the gas safety status into four levels according to the risk from high to low, namely, red, orange, yellow and blue. Since the gas concentration data in each risk level are generated by their specific time sequence characteristics, they can be modeled by a single Gaussian process. Because the general gas concentration data come from four risk levels, the MGP can be used to model the whole data. According to the MGP with the learned parameters, the risk level in each time can be adaptively computed. As for the gas concentration prediction, we can weight the prediction results of four Gaussian processes together to get a more robust prediction. The experimental results demonstrate that the MGP based method can effectively predict the gas concentration and evaluate the safety state.