Semi-supervised　learning　(SSL)　poses　a　significant　practical　challenge　in　the　field　of　computer　vision.　Pseudo　Labeling　methods　(PL　methods),　as　representative　SSL　techniques,　obtain　the　State　Of　The　Art　(SOTA)　performances　in　SSL.　However,　the　error　accumulation　phenomenon　that　easily　occurs　during　their　self-training　process　results　in　high　bias　and　variance　in　pseudo-labels,　further　hindering　effective　model　training.　To　overcome　this　issue,　we　propose　a　feature-based　perturbation　method　for　ensemble　learning　method　to　make　it　more　effective　and　efficient　in　SSL.　By　perturbing　the　features　input　to　each　prediction　head　within　a　multi-head　ensemble　structure,　the　method　reduces　the　prediction　correlation　among　prediction　heads,　thereby　enhancing　ensemble　gain.　It　is　worth　emphasizing　that　the　proposed　method　is　highly　generalizable　and　can　be　easily　extended　to　arbitrary　SSL　frameworks.　Experimental　data　demonstrate　that　our　method　outperforms　the　current　state-of-the-art　techniques　on　the　CIFAR10　dataset　and　significantly　improves　the　quality　of　pseudo-labels.　©　2024　ACM.