With　the　development　of　digitization,　more　and　more　sensors　or　monitoring　tools　are　used　in　all　walks　of　life.　These　devices　generate　a　large　amount　of　time　series　data　every　day.　It　is　of　great　significance　to　process　these　data　in　a　timely　and　efficient　manner.　Deep　neural　networks　have　been　proven　to　perform　good　feature　extraction　and　modeling　for　time　series,　and　good　results　have　been　achieved　in　time　series　classification　problems　through　stacking　of　basic　modules　such　as　multilayer　perceptrons,　fully　convolutional　networks,　and　deep　residual　networks.　However,　the　generalization　of　these　methods　is　poor,　and　the　extraction　of　pattern　features　is　limited.　At　the　same　time,　because　these　modules　cannot　extract　temporal　correlation　features　well,　there　is　still　room　for　improvement　in　classification　accuracy.　Based　on　the　dual　consideration　of　temporal　correlation　features　and　pattern　features,　we　designed　a　time　series　classification　method　based　on　two-channel　feature　extraction　based　on　gating　mechanism　fusion.　Through　extensive　experiments　on　multiple　datasets　from　different　domains,　our　classification　method　proposed　in　this　paper　achieves　better　performance,　and　has　significant　advantages　in　classification　accuracy　and　generalization.　©　2022　IEEE.