In　recent　years,　the　application　of　integrated　circuits　in　various　fields　has　become　more　and　more　extensive.　The　attackers　utilize　the　hidden　dangers　of　the　industrial　chain　to　design　and　launch　hardware　Trojan　attacks　and　their　attack　patterns　and　attack　surfaces　are　also　evolving,　which　has　become　an　emerging　security　threat　in　the　production　of　modern　integrated　circuits.　Traditional　detection　methods　have　made　it　difficult　to　deal　with　hardware　Trojans　in　a　timely　and　effective　manner,　so　new　detection　methods　are　imminent.　In　this　paper,　a　deep　learning-based　hardware　Trojan　detection　method　to　improve　the　accuracy　of　hardware　Trojan　detection　to　some　extent　is　proposed.　The　paper　designs　a　convolutional　neural　network　(TextCNN),　a　bidirectional　long　and　short-term　memory　network　(Bi-LSTM),　and　the　integrated　learning　of　these　two　single　models　(TextCNNLSTM)　as　the　hardware　Trojan　detection　scheme.　The　experimental　results　show　that　the　hardware　Trojan　detection　method　based　on　deep　learning　proposed　in　this　paper　has　advantages　in　terms　of　detection　accuracy　and　robustness.　©　2023　ACM.