Face　super　resolution　(FSR)　is　a　sub-field　of　super　resolution　(SR),　which　is　to　reconstruct　low　resolution　(LR)　face　image　into　high　resolution　(HR)　face　image.　Recently,　the　FSR　methods　based　on　face　prior　have　been　proved　to　be　effective　in　FSR　on　higher　upscaling　factors.　However,　existing　prior　guided　methods　mostly　adopt　supervised　prior　extraction　models　trained　with　labels.　The　performance　of　supervised　prior　extraction　method　mainly　depends　on　the　accuracy　of　label　so　that　the　implicit　informations　of　data　are　not　fully　utilized.　And　in　practical　application,　the　label　acquisition　work　is　routine　and　laborious.　Therefore,　to　solve　these　problems,　this　paper　proposes　a　novel　contrastive　learning　(CL)　based　FSR　method,　which　is　based　on　the　iterative　collaboration　of　image　reconstruction　network　and　contrastive　learning　network.　In　each　iteration,　the　reconstruction　network　uses　the　priors　generated　by　the　contrastive　learning　network　to　assist　the　image　reconstruction　and　generates　higher-quality　SR　images.　Then,　the　SR　image　will　feed　into　contrastive　learning　network　to　obtain　more　accurate　prior.　In　addition,　a　new　contrastive　learning　constraint　function　is　designed　to　extract　the　representation　of　the　augmented　facial　image　as　a　prior　by　analysing　the　principal　component　information　of　the　image.　Quantitative　and　qualitative　experimental　results　show　that　the　proposed　method　is　superior　to　the　most　advanced　FSR　method　in　high-quality　face　images　super　resolution　reconstruction.