The　emergence　of　malicious　Twitter　social　bots　poses　a　considerable　threat　to　the　security　of　social　networks,　and　the　detection　of　evolving　social　bots　has　become　challenging.　State-of-the-art　detection　methods　are　usually　supervised,　but　the　label　acquisition　process　suffers　from　time-consuming　and　inaccurate　problems　in　addition　to　its　inability　to　cope　with　the　challenge　of　the　continuous　evolution　of　social　bots.　Moreover,　structural　relationships　within　social　networks　are　under-explored　using　current　unsupervised　methods.　To　address　current　challenges,　we　propose　an　unsupervised　social　bot　detection　method　based　on　deep　contrastive　graph　clustering　(BotDCGC).　This　approach　utilises　a　graph　attentional　encoder　and　an　inner　product　decoder　to　acquire　user　node　embeddings.　By　incorporating　information　from　both　user　account　features　and　topological　structure,　the　model　employs　a　contrastive　learning　technique　based　on　structure　to　discern　node　embeddings　of　distinct　classes　within　the　feature　space.　Subsequently,　confident　cluster　assignments　are　used　as　soft　labels　to　guide　the　embedding　process　by　calculating　the　similarity　between　each　node　and　the　clustering　centre,　enabling　the　joint　optimisation　of　user　node　embeddings　and　clustering　results.　Experiments　show　that　BotDCGC　is　more　effective　than　the　state-of-the-art　baselines　with　an　accuracy　of　0.8095　in　the　Twibot-20　dataset　and　0.9334　in　the　Cresci2015　dataset,　and　the　experimental　results　indicate　the　effectiveness　of　the　graph　autoencoder　module,　contrastive　learning　and　deep　clustering　module.