Effective　methods　of　evaluation　of　psychological　pressure　can　detect　and　assess　real-time　stress　states,　warning　people　to　pay　necessary　attention　to　their　health.　This　study　is　focused　on　the　stress　assessment　issue　using　an　improved　support　vector　machine　(SVM)　algorithm　based　on　surface　electromyographic　signals.　After　the　samples　were　clustered,　the　cluster　results　were　fed　to　the　loss　function　of　the　SVM　to　screen　training　samples.　With　the　imbalance　among　the　training　samples　after　screening,　a　weight　was　given　to　the　loss　function　to　reduce　the　prediction　tendentiousness　of　the　classifier　and,　therefore,　to　decrease　the　error　of　the　training　sample　and　make　up　for　the　influence　of　the　unbalanced　samples.　This　improved　the　algorithm,　increased　the　classification　accuracy　from　73.79%　to　81.38%,　and　reduced　the　running　time　from　1973.1　to　540.2　sec.　The　experimental　results　show　that　this　algorithm　can　help　to　effectively　avoid　the　influence　of　individual　differences　on　the　stress　appraisal　effect　and　to　reduce　the　computational　complexity　during　the　training　phase　of　the　classifier.