Hand　gesture　recognition　is　a　challenging　topic　in　the　field　of　computer　vision.　Multimodal　hand　gesture　recognition　based　on　RGB-D　is　with　higher　accuracy　than　that　of　only　RGB　or　depth.　It　is　not　difficult　to　conclude　that　the　gain　originates　from　the　complementary　information　existing　in　the　two　modalities.　However,　in　reality,　multimodal　data　are　not　always　easy　to　acquire　simultaneously,　while　unimodal　RGB　or　depth　hand　gesture　data　are　more　general.　Therefore,　one　hand　gesture　system　is　expected,　in　which　only　unimordal　RGB　or　Depth　data　is　supported　for　testing,　while　multimodal　RGB-D　data　is　available　for　training　so　as　to　attain　the　complementary　information.　Fortunately,　a　kind　of　method　via　multimodal　training　and　unimodal　testing　has　been　proposed.　However,　unimodal　feature　representation　and　cross-modality　transfer　still　need　to　be　further　improved.　To　this　end,　this　paper　proposes　a　new　3D-Ghost　and　Spatial　Attention　Inflated　3D　ConvNet　(3DGSAI)　to　extract　high-quality　features　for　each　modality.　The　baseline　of　3DGSAI　network　is　Inflated　3D　ConvNet　(I3D),　and　two　main　improvements　are　proposed.　One　is　3D-Ghost　module,　and　the　other　is　the　spatial　attention　mechanism.　The　3D-Ghost　module　can　extract　richer　features　for　hand　gesture　representation,　and　the　spatial　attention　mechanism　makes　the　network　pay　more　attention　to　hand　region.　This　paper　also　proposes　an　adaptive　parameter　for　positive　knowledge　transfer,　which　ensures　that　the　transfer　always　occurs　from　the　strong　modality　network　to　the　weak　one.　Extensive　experiments　on　SKIG,　VIVA,　and　NVGesture　datasets　demonstrate　that　our　method　is　competitive　with　the　state　of　the　art.　Especially,　the　performance　of　our　method　reaches　97.87%　on　the　SKIG　dataset　using　only　RGB,　which　is　the　current　best　result.