The　accuracy　of　slope　mechanical　parameter　values　determines　the　accuracy　of　slope　stability　assessments　and　the　effectiveness　of　protective　measures.　Traditional　methods　for　obtaining　geotechnical　parameters　cannot　entirely　take　into　consideration　the　size,　disturbance,　variability,　and　uncertainty　of　mechanical　parameters.　Hence,　we　aimed　to　propose　two　algorithmic　models　for　the　geotechnical　parameters　of　highway　slope　materials　based　on　an　improved　non-dominated　sorting　genetic　algorithm　II　(NSGA-II)　combined　with　a　GA-optimised　backpropagation　(BP)　neural　network　(NN),named　BPGA-　NSGA-II　model　and　improved　BPGA-　NSGA-II　model.The　results　indicated　that　compared　with　the　original　NSGA-II　model,　the　improved　model　showed　a　substantial　shift　in　the　Pareto　front,　and　the　errors　are　smaller　than　the　original　model.Among　the　two　improved　models,　the　improved　BPGA-NSGA-II　model　has　higher　inversion　accuracy.Therefore,　this　study　provides　a　scientifically　effective　method　for　improving　the　accuracy　of　the　geotechnical　parameter　inversion　for　highway　slope　materials.