The　massive　use　of　information　technology　has　brought　certain　security　risks　to　the　industrial　production　process.　In　recent　years,　cyber-physical　attacks　against　industrial　control　systems　have　occurred　frequently.　Anomaly　detection　technology　is　an　essential　technical　means　to　ensure　the　safety　of　industrial　control　systems.　Considering　the　shortcomings　of　traditional　methods　and　to　facilitate　the　timely　analysis　and　location　of　anomalies,　this　study　proposes　a　solution　based　on　the　deep　learning　method　for　industrial　traffic　anomaly　detection　and　attack　classification.　We　use　a　convolutional　neural　network　deep　learning　representation　model　as　the　detection　model.　The　original　one-dimensional　data　are　mapped　using　the　feature　mapping　method　to　make　them　suitable　for　model　processing.　The　deep　learning　method　can　automatically　extract　critical　features　and　achieve　accurate　attack　classification.　We　performed　a　model　evaluation　using　real　network　attack　data　from　a　supervisory　control　and　data　acquisition　(SCADA)　system.　The　experimental　results　showed　that　the　proposed　method　met　the　anomaly　detection　and　attack　classification　needs　of　a　SCADA　system.　The　proposed　method　also　promotes　the　application　of　deep　learning　methods　in　industrial　anomaly　detection.