The data collected by multi-source sensors not only have a lot of redundancy, but also affect the final monitoring results. In order to improve the accuracy of monitoring, this study proposes a two-level data fusion model and algorithm for grassland environment monitoring. In the first-level data fusion, the adaptive weighted averaging method is used to fuse the similar sensors in each region, and then the BP neural network is used to train and fuse the heterogeneous sensors in the region, thus a preliminary judgment on the environmental conditions of each region is obtained. Because of the uncertainty of the fusion result by BP neural network, the secondary fusion uses DES evidence theory to analyze the primary fusion result and get the decision-making judgment of grassland environment. Finally, the validity and analysis of the model and algorithm are carried out. The experimental results show that the proposed method can accurately monitor the grassland environment. At the same time, it provides some valuable guidance and decision-making basis for the efficient management and scientific conservation of grassland environment.