With　the　rapid　growth　of　malicious　codes,　personal　privacy,　and　Internet　security　are　seriously　threatened.　Existing　transfer　learning-based　malicious　code　detection　improves　detection　accuracy　by　transferring　pre-trained　neural　networks.　However,　it　cannot　efficiently　tune　the　structure　and　parameters　of　the　neural　networks.　Here,　we　first　propose　a　novel　many-objective　transfer　model.　It　mainly　focuses　on　the　detection　accuracy　and　the　total　number　of　parameters　of　the　neural　network　model.　The　optimal　structure　and　parameters　are　captured　from　the　pre-trained　neural　network　by　many-objective　optimization　algorithm.　Second,　the　partitioned　crossover-mutation　vector　angle-based　evolutionary　algorithm　for　unconstrained　many-objective　optimization　is　proposed　to　solve　the　model.　The　algorithm　performs　crossover　mutation　operations　in　different　ways　on　different　regions　of　the　candidate　solution　to　improve　population　diversity.　The　simulation　results　show　that　the　model　can　reduce　the　pre-trained　neural　network　structure　by　49%　while　maintaining　the　accuracy　in　malicious　code　detection.