Intrusion　detection　is　only　the　initial　part　of　the　security　system　for　an　industrial　control　system.　Because　of　the　criticality　of　the　industrial　control　system,　professionals　still　make　the　most　important　security　decisions.　Therefore,　a　simple　intrusion　alarm　has　a　very　limited　role　in　the　security　system,　and　intrusion　detection　models　based　on　deep　learning　struggle　to　provide　more　information　because　of　the　lack　of　explanation.　This　limits　the　application　of　deep　learning　methods　to　industrial　control　network　intrusion　detection.　We　analyzed　the　deep　neural　network　(DNN)　model　and　the　interpretable　classification　model　from　the　perspective　of　information,　and　clarified　the　correlation　between　the　calculation　process　of　the　DNN　model　and　the　classification　process.　By　comparing　the　normal　samples　with　the　abnormal　samples,　the　abnormalities　that　occur　during　the　calculation　of　the　DNN　model　compared　to　the　normal　samples　could　be　found.　Based　on　this,　a　layer-wise　relevance　propagation　method　was　designed　to　map　the　abnormalities　in　the　calculation　process　to　the　abnormalities　of　attributes.　At　the　same　time,　considering　that　the　data　set　may　already　contain　some　useful　information,　we　designed　filtering　rules　for　a　kind　of　data　set　that　can　be　obtained　at　a　low　cost,　so　that　the　calculation　result　is　presented　in　a　more　accurate　manner,　which　should　help　professionals　lock　and　address　intrusion　threats　more　quickly.