Objective:　To　optimize　the　baseline　on　the　trapezoidal　cross-section　of　stent　wires,　so　as　to　reduce　the　risk　of　intracranial　saccular　aneurysm　rupture　after　the　implantation　of　such　stents.　Methods:　Thirty-eight　trapezoidal　cross-section　wire　stents　with　different　baselines　were　constructed　to　establish　the　finite　element　models.　Numerical　simulation　by　fluid-solid　interaction　method　was　conducted　to　calculate　38　maximum　pressure　gradients　on　the　aneurysm　wall.　GRNN　(general　regression　neural　network)　and　GA　(genetic　algorithm)　were　used　to　optimize　the　baseline　on　the　cross-section　of　stents　with　trapezoidal　cross-section　wire　so　as　to　minimize　the　maximal　pressure　gradient　on　the　aneurysm　wall.　Results:　Compared　with　the　traditional　stent　with　rectangular　cross-section　wire,　the　maximal　pressure　gradient　on　the　a　neurysm　wall　was　reduced　by　7.86%　after　the　implantation　with　the　optimized　stent　with　trapezoidal　cross-section　wire.　Conclusions:　The　combination　of　GRNN　and　GA　is　an　effective　approach　for　stent　optimization.