Semi-supervised　learning,　a　system　dedicated　to　making　networks　less　dependent　on　labeled　data,　has　become　a　popular　paradigm　due　to　its　strong　performance.　A　common　approach　is　to　use　pseudo-labels　with　unlabeled　data　for　training,　however,　pseudo-labels　cannot　correct　their　own　errors.　In　this　paper,　we　propose　a　semi-supervised　method　that　uses　nearest　neighbor　samples　to　obtain　pseudo-labels　and　combines　consistency　regularization　for　image　classification.　Our　method　obtains　pseudo-labels　by　computing　the　similarity　of　the　data　distribution　between　the　weakly-augmented　version　of　the　unlabeled　data　and　the　labeled　data　stored　in　the　support　set　and　combines　the　consistency　of　the　strongly-augmented　version　and　the　weakly-augmented　version　of　the　unlabeled　data.　We　compared　with　several　standard　semi-supervised　learning　benchmarks　and　achieved　a　competitive　performance.　For　example,　we　achieved　an　accuracy　of　94.02　%　on　CIFAR-10　with　250　labels　and　97.50　%　on　SVNH　with　250　labels.　It　even　achieved　91.59　%　accuracy　with　only　40　labels　data　in　the　CIFAR-10.　©　2023,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Switzerland　AG.