The　correlation　between　multiple　patterns　of　micromagnetic　signatures　and　the　mechanical　properties　(yield　strength　(Rp)　and　tensile　strength　(Rm)　of　high-strength　steel　(referred　to　as　DP590　steel　in　Chinese　standards)　was　investigated　in　this　study.　Feedforward　neural　network　(FF-NN)　models　with　carefully　selected　magnetic　features　as　input　nodes　were　established　for　quantitative　prediction　of　yield　strength　and　tensile　strength.　The　accuracy　FF-NN　models　highly　relied　on　the　quality　of　calibration　specimens　and　the　way　of　selecting　magnetic　features.　The　variations　of　the　measured　target　properties　were　used　to　evaluate　the　quality　of　the　calibration　specimens.　The　specimens　with　similar　yield　strength　(or　tensile　strength)　were　merged　to　share　the　same　target　properties　in　the　model　training　process.　The　results　demonstrated　that　merging　proper　target　properties　(label)　could　improve　the　performance　of　the　models　in　quantitative　prediction　of　yield　strength　and　tensile　strength　in　DP590　steels.　In　addition,　the　performances　of　FF-NN　models　combined　with　the　algorithms　of　ReliefF　and　ReliefF　+　clustering　were　evaluated.　The　comparison　results　proved　that　the　FF-NN　models　employing　input　nodes　selection　strategy　of　ReliefF　+　clustering　realized　the　advantages　of　smaller　dimensions　of　input　nodes,　less　training　time　consumption　at　the　cost　of　slight　accuracy　reduction.