This　work　proposes　an　intelligent　grey-wolf-optimizer-improved　Apriori　algorithm　(GWO-Apriori)　to　mine　the　association　rules　of　hidden　dangers　in　hydrogen　pipeline　transmission　stations.　The　optimal　minimum　support　and　minimum　confidence　are　determined　by　GWO　instead　of　the　time-consuming　trial　approach.　Experiments　show　that　the　average　support　and　average　confidence　of　association　rules　using　GWO-Apriori　increase　by　29.8%　and　21.3%,　respectively,　when　compared　with　traditional　Apriori.　Overall,　59　ineffective　association　rules　out　of　the　total　105　rules　are　filtered　by　GWO,　which　dramatically　improves　data　mining　effectiveness.　Moreover,　23　illogical　association　rules　are　excluded,　and　12　new　strong　association　rules　ignored　by　the　traditional　Apriori　are　successfully　mined.　Compared　with　the　inefficient　and　labor-intensive　manual　investigation,　the　intelligent　GWO-Apriori　algorithm　dramatically　improves　pertinency　and　efficiency　of　hidden　danger　identification　in　hydrogen　pipeline　transmission　stations.　©　2024　by　the　authors.