At　present,　the　research　on　gesture　recognition　based　on　ultrasonic　signals　is　mostly　about　static　gestures.　Compared　with　static　gestures,　dynamic　gestures　reflect　human　intent　more　intuitively.　However,　the　speed　difference　of　dynamic　gestures　will　cause　a　mismatch　in　time　series.　To　tackle　this　problem,　we　proposed　a　dynamic　gesture　recognition　scheme　based　on　the　dynamic　time　warping　(DTW)　algorithm.　First,　the　ultrasonic　signals　of　the　forearm　muscles　were　acquired　by　four　A-mode　ultrasonic　transducers.　Second,　the　original　signals　underwent　Gaussian　filtering,　feature　extraction,　normalization,　and　principal　component　analysis　(PCA)　dimensionality　reduction　to　obtain　the　main　feature　information　and　convert　it　into　time　series.　Finally,　the　time　series　of　dynamic　gestures　were　matched　based　on　the　DTW　algorithm,　and　the　minimum　Euclidean　distance　between　different　time　series　was　calculated　for　prediction.　We　tested　five　dynamic　gestures　on　ten　subjects.　The　experiments　were　conducted　for　a　total　of　ten　rounds,　and　only　one　round　was　used　as　the　training　set.　Results　reveal　that　using　the　max　feature　can　get　an　accuracy　of　88.0%　+/-　5.9%.　In　addition,　the　energy　feature　(87.5%　+/-　6.4%)　and　the　mean　feature　(85.2%　+/-　6.9%)　also　performed　well.　This　research　realizes　the　dynamic　gesture　recognition　of　A-mode　ultrasonic　for　the　first　time,　proving　the　potential　of　ultrasonic　signals,　and　providing　a　wider　range　of　use　scenarios.