To address the issues of decreased product quality and abnormal conditions caused by variations in raw coal properties and parameter fluctuations in coal slurry flotation process, this paper proposes an integrated safe operation and product quality control method for the coal slurry flotation process based on a distributed hybrid Bayesian network (BN). The method divides the coal slurry flotation process into interrelated local modules and establishes corresponding local hybrid BN models. Then, by utilizing the correlated variables, the global hybrid BN model of the coal slurry flotation process is determined, effectively improving the efficiency and accuracy of modeling. The model improves the inference accuracy of control decisions by introducing continuous nodes based on the discrete BN. When abnormal conditions occur in the coal slurry flotation process, the contribution graph algorithm is used to identify the local modules causing the abnormal conditions. The BN inference is then employed to obtain safe operation control decisions, eliminating the abnormal conditions. Furthermore, by combining the simulated annealing algorithm on this basis, product quality control decisions are obtained to improve the coal product quality. Finally, the effectiveness of the proposed method is validated through the simulation experiments of the coal slurry flotation process.