In　recent　years,　botnets　have　used　the　domain　generation　algorithm　to　generate　dynamic　typified　malicious　domain　names　to　bypass　various　detection　methods.　Given　the　depth　detection　model　of　such　domain　names,　domain　names　are　generally　processed　by　filling　and　transforming　them　into　a　fixed-length　one-dimensional　vector　and　then　classifying　them　with　poor　detection　performance.　Therefore,　this　study　first　divides　the　domain　into　a　word　array　and　converts　it　into　a　word　vector　using　pre-trained　word　vector　models,　Embeddings　from　Language　Models.　The　domain　is　inputted　into　the　TextCNN　model　for　training　classification.　From　approximately　100,000　data　sets,　a　94.22%　accuracy　rate　and　6.87%　FPR　value　can　be　obtained　from　the　training.　Compared　with　previous　detection　models　(i.e.,　LSTM　and　CNN),　more　training　and　testing　are　needed,　but　improvements　are　made　in　all　indicators.　©　Published　under　licence　by　IOP　Publishing　Ltd.