Human　gesture　recognition　is　an　important　research　field　of　human-computer　interaction　due　to　its　potential　applications　in　various　fields,　but　existing　methods　still　face　challenges　in　achieving　high　levels　of　accuracy.　To　address　this　issue,　some　existing　researches　propose　to　fuse　the　global　features　with　the　cropped　features　called　focuses　on　vital　body　parts　like　hands.　However,　most　methods　rely　on　experience　when　choosing　the　focus,　the　scheme　of　focus　selection　is　not　discussed　in　detail.　In　this　paper,　a　hierarchical　body　part　combination　method　is　proposed　to　take　into　account　the　number,　combinations,　and　logical　relationships　between　body　parts.　The　proposed　method　generates　multiple　focuses　using　this　method　and　employs　chart-based　surface　modality　alongside　red-green-blue　and　optical　flow　modalities　to　enhance　each　focus.　A　feature-level　fusion　scheme　based　on　the　residual　connection　structure　is　proposed　to　fuse　different　modalities　at　convolution　stages,　and　a　focus　fusion　scheme　is　proposed　to　learn　the　relevancy　of　focus　channels　for　each　gesture　class　individually.　Experiments　conducted　on　ChaLearn　isolated　gesture　dataset　show　that　the　use　of　multiple　focuses　in　conjunction　with　multi-modal　features　and　fusion　strategies　leads　to　better　gesture　recognition　accuracy.