Sulfide　ores,　collectively　referred　to　as　a　class　of　minerals　with　extensive　applications,　are　increasingly　susceptible　to　spontaneous　combustion　incidents　as　the　mining　environment　evolves.　The　spontaneous　combustion　of　sulfide　ores　is　a　complex　nonlinear　process,　and　the　mechanisms　of　which　remain　not　fully　understood.　Consequently,　it　is　challenging　to　delineate　the　accident　trajectory　through　traditional　accident　causation　models　and　subsequently　construct　a　probabilistic　model　for　spontaneous　combustion　risk.　To　address　this　issue,　this　paper　proposed　a　risk　assessment　model　based　on　grounded　theory　and　Bayesian　network.　The　advantage　of　this　model　is　that　it　employs　grounded　theory　to　categorize　and　organize　factors　influencing　the　occurrence　of　accidents,　thereby　forming　a　clear　accident　trajectory　and　providing　a　framework　and　basis　for　constructing　a　probabilistic　assessment　model.　Furthermore,　the　model　presented　in　this　paper　introduces　the　D　number　theory　and　the　Noisy-OR　model　to　handle　uncertainties　in　the　risk　assessment　process　and　to　establish　more　rational　conditional　probability　tables.　The　findings　suggest　that　environmental　temperature,　heat　dissipation　conditions,　safety　awareness,　and　safety　skills　are　the　most　influential　factors　in　spontaneous　combustion　incidents　when　considering　a　particular　point　in　time.　However,　when　considering　the　entire　process　of　sulfide　ore　pile　spontaneous　combustion,　the　temperature　of　the　ore　pile　is　the　most　critical　accident　factor　to　monitor.　Therefore,　the　key　to　suppressing　the　spontaneous　combustion　of　sulfide　ores　lies　in　reducing　environmental　temperatures,　enhancing　heat　dissipation,　and　elevating　the　safety　awareness　of　relevant　personnel.