As　an　important　part　of　the　unmanned　driving　system,　the　detection　and　recognition　of　traffic　sign　need　to　have　the　characteristics　of　excellent　recognition　accuracy,　fast　execution　speed　and　easy　deployment.　Researchers　have　applied　the　techniques　of　machine　learning,　deep　learning　and　image　processing　to　traffic　sign　recognition　successfully.　Considering　the　hardware　conditions　of　the　terminal　equipment　in　the　unmanned　driving　system,　in　this　research　work,　the　goal　was　to　achieve　a　convolutional　neural　network　(CNN)　architecture　that　is　lightweight　and　easily　implemented　for　an　embedded　application　and　with　excellent　recognition　accuracy　and　execution　speed.　As　a　classical　CNN　architecture,　LeNet-5　network　model　was　chosen　to　be　improved,　including　image　preprocessing,　improving　spatial　pool　convolutional　neural　network,　optimizing　neurons,　optimizing　activation　function,　etc.　The　test　experiment　of　the　improved　network　architecture　was　carried　out　on　German　Traffic　Sign　Recognition　Benchmark　(GTSRB)　database.　The　experimental　results　show　that　the　improved　network　architecture　can　obtain　higher　recognition　accuracy　in　a　short　interference　time,　and　the　algorithm　loss　is　significantly　reduced　with　the　progress　of　training.　At　the　same　time,　compared　with　other　lightweight　network　models,　this　network　architecture　gives　a　good　recognition　result,　with　a　recognition　accuracy　of　97.53%.　The　network　structure　is　simple,　the　algorithm　complexity　is　low,　and　it　is　suitable　for　all　kinds　of　terminal　equipment,　which　can　have　a　wider　application　in　unmanned　driving　system.