The　problem　of　small　sample　size　is　a　major　and　severe　problem　in　the　era　of　big　data.　At　present,　the　common　solution　to　the　problem　of　small　samples　is　to　expand　the　number　of　samples　by　using　virtual　sample　generation　(VSG)　technology.　But　there　are　still　many　problems　such　as　rationality,　accuracy　and　practical　application.　Based　on　this,　this　paper　proposes　an　improved　VSG　technology.　At　first,　a　region　expansion　technique　based　on　Euclidean　distance　of　sub-region　is　proposed,　which　extends　the　input　sample　space　and　output　sample　space.　Then,　in　the　extended　region,　the　virtual　input　samples　are　generated　equidistantly.　The　virtual　output　samples　are　generated　by　the　mapping　model　and　the　virtual　input　samples,　and　the　virtual　output　samples　are　deleted　by　combining　the　output　sample　space.　Next,　a　random　weight　neural　network　(RWNN)　is　used　to　generate　virtual　input　and　output　samples　based　on　its　hidden　layer.　Finally,　these　true　and　virtual　samples　are　mixed　to　produce　the　final　modeling.　The　validity　and　rationality　of　the　VSG　technology　are　validated　by　benchmark　data　set.