Neural　network　pruning　is　a　critical　field　aimed　at　reducing　the　infrastructure　costs　of　neural　networks　by　removing　parameters.　Traditional　methods　follow　a　fixed　paradigm　including　pretraining,　pruning,　and　fine-tuning.　Despite　the　close　relationship　among　these　three　stages,　most　pruning　methods　treat　them　as　independent　processes.　In　this　paper,　we　propose　a　novel　two-stage　pruning　method,　which　includes　pretraining　a　network　that　is　instructive　for　subsequent　pruning,　and　a　unified　optimization　model　that　integrates　pruning　and　fine-tuning.　Specifically,　in　the　first　stage,　we　design　a　group　sparse　regularized　model　for　pretraining.　This　model　not　only　safeguards　the　network　from　irreversible　damage　but　also　offers　valuable　insights　for　the　pruning　process.　In　the　second　stage,　we　introduce　an　element-wise　sparse　regularization　into　pruning　model.　This　model　enables　us　to　pinpoint　sparse　weights　more　precisely　than　pretrained　network.　It　automatically　derives　effective　pruning　criteria,　and　omits　the　step　of　fine-tuning.　To　implement　the　two-stage　process　in　practice,　we　utilize　stochastic　gradient　algorithm　for　the　pretraining　and　design　a　threshold　algorithm　for　pruning　stage.　Extensive　experiments　confirm　the　competitive　performance　of　our　proposed　method　in　terms　of　both　accuracy　and　memory　cost　when　compared　to　various　benchmarks.　Furthermore,　ablation　experiments　validate　the　effectiveness　of　the　proposed　pretraining　model＇s　guidance　for　the　pruning　process.