The　performance　of　underwater　target　detection　techniques　is　limited　by　various　factors.　First,　in　underwater　image　data　sets,　there　exists　degradation　such　as　color　bias,　low　contrast,　and　blurring,　which　affect　the　accuracy　of　the　detection　algorithms.　Second,　the　underwater　image　data　set　is　difficult　to　obtain　and　the　cost　of　making　the　labeled　data　sets　is　high,　which　also　prevents　underwater　object　detection　algorithms　from　fully　leveraging　their　potential.　In　this　article,　we　propose　a　self-supervised　learning　network　for　underwater　target　detection.　Considering　the　deficiency　that　the　conventional　contrastive　learning　network　pays　more　attention　to　the　global　information　and　ignores　the　local　information,　an　auxiliary　branch　inspired　by　masked　autoencoders　is　added　to　the　baseline　SimSiam　network,　which　collaborates　with　the　main　branch　to　optimize　the　target　network　and　help　the　target　network　learn　the　local　information　of　the　target　feature　map.　A　residual　spatial　cooperative　attention　module　is　proposed　to　be　embedded　within　the　proposed　self-supervised　learning　network　to　obtain　remote　information　through　residual　structure　and　construct　spatial　context　features.　The　method　of　cooperative　attention　is　used　to　enhance　feature　learning　ability.　Experiments　are　carried　out　on　a　reconstructed　underwater　target　data　set.　Results　show　that　compared　with　the　baseline　network,　the　method　proposed　in　this　article　is　more　suitable　for　underwater　environments　and　has　better　mean　average　precision.