Meta-learning　has　been　widely　applied　and　achieved　certain　results　in　few-shot　cross-domain　fault　diagnosis　due　to　its　powerful　generalization　and　robustness.　However,　existing　meta-learning　methods　mainly　focus　on　cross-domain　fault　diagnosis　within　the　same　machine,　ignoring　the　fact　that　there　are　more　significant　domain　distribution　differences　and　sample　imbalance　problems　between　different　machines,　leading　to　poor　diagnostic　performance.　This　paper　proposes　a　semi-supervised　prototypical　network　with　dual　correction　to　address　these　issues.　First,　a　dual-channel　residual　network　is　utilized　to　comprehensively　extract　sample　features,　capturing　deep　and　shallow　information.　Then,　correct　the　semi-supervised　prototypical　network　by　weighting　the　features　and　adding　a　shift　term　on　support　set　samples　and　query　set　samples,　respectively,　to　diminish　its　intra-class　and　extra-class　bias.　Meanwhile,　a　regularization　term　is　introduced　into　the　model　to　balance　the　distribution　among　different　class　prototypes,　enhancing　distinctiveness.　Finally,　few-shot　cross-machine　fault　diagnosis　experiments　are　conducted　on　three　datasets　to　validate　the　method＇s　effectiveness.　Additionally,　an　interpretability　analysis　of　the　model　is　conducted　using　the　gradient-weighted　class　activation　mapping　(Grad-CAM)　technique　to　discern　its　primary　regions　of　focus　in　the　classification　tasks.