In　the　selective　laser　melting　(SLM)　process,　the　experimental　approach　to　determine　the　optimal　process　parameters　is　labor-intensive,　material-intensive,　and　time-consuming.　The　use　of　simulation　methods　also　requires　more　time　support　and　higher　hardware　requirements.　In　this　paper,　a　three-dimensional　transient　heat　transfer　model　and　a　neural　network　optimization　process　parameter　model　in　the　process　of　preparing　copper　alloys　by　SLM　are　developed　by　combining　finite　element　simulation　methods　with　neural　network　prediction.　The　thermal　behavior　of　the　multitrack　molten　pools　was　investigated　by　ANSYS　APDL,　and　the　effects　of　different　laser　powers　and　scanning　speeds　on　the　temperature　field　and　structure　dimensions　of　the　molten　pools　were　discussed.　The　results　show　that　the　current　single-track　has　a　significant　preheating　effect　on　the　unmachined　single-track　and　a　reheating　effect　on　the　machined　single-track　during　the　multitrack　forming　process.　The　laser　power　and　scanning　speed　can　be　controlled　to　regulate　the　temperature,　3D　size,　and　heat　spread　area　of　the　molten　pool　to　avoid　over-melting　and　under-melting.　The　accuracy　of　the　temperature　field　model　was　verified　by　single-track　experiments.　A　neural　network　prediction　model　was　constructed　to　predict　the　maximum　temperature　and　size　of　the　molten　pool　by　optimizing　the　backpropagation　neural　network　with　a　genetic　algorithm,　providing　a　methodological　guide　for　the　study　of　SLM　process　parameters.