The　hand　gesture　recognition　(HGR)　technology　in　A-mode　ultrasound　human-machine　interface　(HMI-A),　based　on　traditional　machine　learning,　relies　on　intricate　feature　reduction　methods.　Researchers　need　prior　knowledge　and　multiple　validations　to　achieve　the　optimal　combination　of　features　and　machine　learning　algorithms.　Furthermore,　anatomical　differences　in　the　forearm　muscles　among　different　subjects　prevent　specific　subject　models　from　applying　to　unknown　subjects,　necessitating　repetitive　retraining.　This　increases　users＇　time　costs　and　limits　the　real-world　application　of　HMI-A.　Hence,　this　article　presents　a　lightweight　1-D　four-branch　squeeze-to-excitation　convolutional　neural　network　(CNN)　(4-branch　SENet)　that　outperforms　traditional　machine　learning　methods　in　both　feature　extraction　and　gesture　classification.　Building　upon　this,　a　weight　fine-tuning　strategy　using　transfer　learning　enables　rapid　gesture　recognition　across　subjects　and　time.　Comparative　analysis　indicates　that　the　freeze　feature　and　fine-tuning　fully　connected　(FC)　layers　result　in　an　average　accuracy　of　96.35%　+/-　3.04%　and　an　average　runtime　of　4.8　+/-　0.15　s,　making　it　52.9%　faster　than　subject-specific　models.　This　method　further　extends　the　application　scenarios　of　HMI-A　in　fields　such　as　medical　rehabilitation　and　intelligent　prosthetics.