The　high　reliability　of　seawater　hydraulic　components　depends　on　the　tribological　properties　of　key　tribopairs.　It　is　impractical　to　conduct　a　high　number　of　lifetime　tests　in　a　short　amount　of　time　due　to　the　limitation　of　the　cost　and　test　time　constraints　in　practical　engineering　applications,　which　has　a　significantly　impact　on　the　accuracy　of　reliability　evaluation.　To　solve　this,　the　Generative　Adversarial　Network　(GAN)　is　provided　as　a　few-shot　learning　(FSL)　technique　for　expanding　the　tribopairs　degradation　data　set,　which　has　the　potential　to　generate　high-quality　samples.　Considering　the　uncertainty　and　diversity,　an　active　learning　approach　is　proposed　to　selecting　the　most　effective　generated　samples.　Finally,　reliability　is　finally　evaluated　based　on　Wiener　process　model　via　using　actual　data　combine　with　generated　data　selected　from　GAN　model.　Simulations　and　friction　characteristics　experiment　of　polyether　ether　ketone　(PEEK)/174PH　stainless　steel　are　applied　to　validate　the　proposed　method,　the　results　show　that　the　proposed　method　could　generate　and　choose　samples　that　are　highly　similar　to　the　real　samples,　the　accuracy　of　reliability　evaluation　is　effectively　improved.