Mobile　edge　computing　(MEC)　is　an　effective　computing　tool　to　cope　with　the　explosive　growth　of　data　traffic.　It　plays　a　vital　role　in　improving　the　quality　of　service　for　user　task　computing.　However,　the　existing　solutions　rarely　address　all　the　significant　factors　that　impact　the　quality　of　service.　To　challenge　this　problem,　a　trusted　many-objective　model　is　built　by　comprehensively　considering　the　task　time　delay,　server　energy　consumption,　trust　metrics　between　task　and　server,　and　user　experience　utility　factors　in　multi-server　MEC　networks.　We　decompose　the　original　problem　into　task　offloading　(TO)　and　resource　allocation　(RA)　to　address　the　model.　Then　a　novel　hybrid　many-objective　optimization　algorithm　based　on　cascading　clustering　and　incremental　learning　is　designed　to　optimize　the　TO　decision　solutions.　A　low-complexity　heuristic　method　is　adopted　based　on　the　optimal　TO　decision　solutions　to　optimize　the　RA　problem　continuously.　To　verify　the　model's　validity　and　the　optimisation　algorithm's　superiority,　five　other　advanced　many-objective　algorithms　are　used　for　comparison.　The　results　show　that　our　algorithm　has　more　than　half　the　number　of　the　superior　values　for　the　benchmark　problem.　And　the　obtained　model　solution　shows　good　performance　on　different　indicators　metrics　for　the　decomposition　problem.　IEEE