Semi-supervised　learning　is　a　common　way　that　investigates　how　to　improve　performance　of　a　visual　learning　model,　while　data　annotation　is　far　from　sufficient.　Recent　works　in　semi-supervised　deep　learning　have　successfully　applied　consistency　regularization,　which　encourages　a　model　to　maintain　consistent　predictions　for　different　perturbed　versions　of　an　image.　However,　most　of　such　methods　ignore　the　category　correlation　of　image　features,　especially　when　exploiting　strong　augmentation　methods　for　unlabeled　images.　To　address　this　problem,　we　propose　PConMatch,　a　model　that　leverages　a　probabilistic　contrastive　learning　framework　to　separate　the　features　of　strongly-augmented　versions　from　different　classes.　A　semi-supervised　probabilistic　contrastive　loss　is　designed,　which　takes　both　labeled　and　unlabeled　samples　into　account　and　develops　an　auxiliary　module　to　generate　a　probability　score　to　measure　the　model　prediction　confidence　for　each　sample.　Specifically,　PConMatch　first　generates　a　pair　of　weakly-augmented　versions　for　each　labeled　sample,　and　produces　a　weakly-augmented　version　and　a　corresponding　pair　of　strongly-augmented　versions　for　each　unlabeled　sample.　Second,　a　probability　score　module　is　proposed　to　assign　pseudo-labeling　confidence　scores　to　strongly-augmented　unlabeled　images.　Finally,　the　probability　score　of　each　sample　is　further　passed　to　the　contrastive　loss,　combining　with　consistency　regularization　to　enable　the　model　to　learn　better　feature　representations.　Extensive　experiments　on　four　publicly　available　image　classification　benchmarks　demonstrate　that　the　proposed　approach　achieves　state-of-the-art　performance　in　image　classification.　Several　rigorous　ablation　studies　are　conducted　to　validate　the　effectiveness　of　the　method.