The　class　imbalance　problem　is　one　of　the　critical　research　areas　of　machine　learning　and　deep　learning　and　has　received　widespread　attention　from　researchers.　To　solve　the　class　imbalance　problem,　current　typical　methods　only　use　positive　samples　to　generate　synthetic　samples　that　are　similar　to　the　minority　class　while　ignoring　the　characteristic　information　of　negative　samples.　Therefore,　when　the　number　of　positive　samples　is　too　small　and　has　highly　similar　features,　it　will　cause　the　classifier　to　have　fitting　problems.　In　response　to　the　above　problems,　we　propose　a　new　positive　sample　enhancement　algorithm　(PENH)　to　solve　the　class　imbalance　by　simulating　the　process　of　chromosome　cross-fusion.　We　select　the　fuzzy　negative　sample　set　around　the　positive　sample　by　the　K-nearest　neighbor　algorithm　and　adopt　the　beyond　empirical　risk　minimization　(Mixup)　to　randomly　hybridize　the　positive　sample　with　the　negative　sample　of　the　set.　To　overcome　the　problem　of　sample　imbalance,　we　adopt　the　One-class　SVM　with　overfitting　of　positive　samples　to　select　the　newly　generated　unlabeled　samples　to　obtain　the　balanced　dataset.　We　construct　multiple　experiments　in　20　open　datasets.　The　results　show　that　our　PENH　outperforms　the　other　six　baseline　methods　in　multiple　evaluation　indicator.