In　order　to　accelerate　the　learning　ability　of　neural　network　structure　parameters　and　improve　the　prediction　accuracy　of　deep　learning　algorithms,　an　evolutionary　algorithm,　based　on　a　prior　Gaussian　mutation　(PGM)　operator,　is　proposed　to　optimize　the　structure　parameters　of　a　gated　recurrent　unit　(GRU)　neural　network.　In　this　algorithm,　the　sensitivity　learning　process　of　GRU　model　parameters　into　the　Gaussian　mutation　operator,　used　the　variance　of　the　GRU　model　parameter　training　results　as　the　Gaussian　mutation　variance　to　generate　the　optimal　individual　candidate　set.　Then,　the　optimal　GRU　neural　network　structure　is　constructed　using　the　evolutionary　algorithm　of　the　prior　Gaussian　mutation　operator.　Moreover,　the　PGM-EA-GRU　algorithm　is　applied　to　the　prediction　of　stock　market　returns.　Experiments　show　that　the　prediction　model　effectively　overcomes　the　GRU　neural　network,　quickly　falling　into　a　local　optimum　and　slowly　converging.　Compared　to　the　RF,　SVR,　RNN,　LSTM,　GRU,　and　EA-GRU　benchmark　models,　the　model　significantly　improves　the　searchability　and　prediction　accuracy　of　the　optimal　network　structure　parameters.　It　also　validates　the　effectiveness　and　the　progressive　nature　of　the　PGM-EA-GRU　model　proposed　in　this　paper　with　stock　market　return　prediction.