Hand　gesture　recognition(HGR)　is　a　key　technology　in　human-computer　interaction　and　human　communication.　This　paper　presents　a　lightweight,　parameter-free　attention　convolutional　neural　network　(LPA-CNN)　approach　leveraging　Gramian　Angular　Field(GAF)transformation　of　A-mode　ultrasound　signals　for　HGR.　First,　this　paper　maps　1-dimensional　(1D)　A-mode　ultrasound　signals,　collected　from　the　forearm　muscles　of　10　healthy　participants,　into　2-dimensional　(2D)　images.　Second,　GAF　is　selected　owing　to　its　higher　sensitivity　against　Markov　Transition　Field　(MTF)　and　Recurrence　Plot　(RP)　in　HGR.　Third,　a　novel　LPA-CNN　consisting　of　four　components,　i.e.,　a　convolution-pooling　block,　an　attention　mechanism,　an　inverted　residual　block,　and　a　classification　block,　is　proposed.　Among　them,　the　convolution-pooling　block　consists　of　convolutional　and　pooling　layers,　the　attention　mechanism　is　applied　to　generate　3-D　weights,　the　inverted　residual　block　consists　of　multiple　channel　shuffling　units,　and　the　classification　block　is　performed　through　fully　connected　layers.　Fourth,　comparative　experiments　were　conducted　on　GoogLeNet,　MobileNet,　and　LPA-CNN　to　validate　the　effectiveness　of　the　proposed　method.　Experimental　results　show　that　compared　to　GoogLeNet　and　MobileNet,　LPA-CNN　has　a　smaller　model　size　and　better　recognition　performance,　achieving　a　classification　accuracy　of　0.98　+/-　0.02.　This　paper　achieves　efficient　and　high-accuracy　HGR　by　encoding　A-mode　ultrasound　signals　into　2D　images　and　integrating　the　LPA-CNN　model,　providing　a　new　technological　approach　for　HGR　based　on　ultrasonic　signals.