When exploiting Jurassic-era coal resources in Northwest China, there are risks of water inrush and sand burst disasters from coal seam roofs. To improve the safety of coal mining, it is imperative to accurately and objectively evaluate the water inrush risk of sandstone aquifers from coal seam roofs and to reasonably and effectively prevent and control water disasters. In this paper, the 221 mining area of the Shilawusu Coal Mine was considered. By combining the basic geological condition data, hydrogeological condition data, and drilling data in the area studied, four main control factors, including the equivalent thickness of sandstone, the lithology coefficient of sandstone, the interbedded coefficient of sand and mud, and the core recovery rate, were selected as evaluation indexes for predicting the water inrush risk from the coal seam roof. A hierarchical prediction and discrimination model of water inrush risk based on combination weighting-set pair analysis was established. The combination weighting method, which is based on the sum of squared deviations, was used to optimize the subjective and objective weight values obtained by the improved analytic hierarchy process and entropy weight methods. By applying set pair analysis theory, the comprehensive connection degree was determined using the set pair connection degree function that was constructed with 31 instances of drilling data in the study area. Then, the risk grade of each drilling data instance was evaluated by the confidence criterion of set pair analysis to calculate the water inrush risk evaluation index. Finally, the obtained index was combined with the borehole pumping test data and the discharging test data to partition the water inrush risk from the coal seam roof. The results indicated that most of the 221 mining area is safe, and the small transitional and dangerous areas are only in the central and northern regions. Based on the combination weighting-set pair analysis method, the water inrush risk from the coal seam roofs in the study area was accurately and objectively classified by a discrimination model.