Motion　blur　can　easily　affect　the　quality　of　QR　code　image,　making　it　difficult　to　recognize　QR　codes　on　moving　objects.　This　paper　proposes　an　algorithm　for　the　recognition　of　motion-blurred　QR　codes　based　on　generative　adversarial　network　and　attention　mechanism.　Firstly,　a　multi-scale　feature　extraction　framework　for　motion　defuzzification　is　designed　using　deep　convolutional　neural　networks,　and　enhanced　multi-scale　residual　blocks　and　multi-scale　feature　extraction　modules　are　utilized　to　capture　rich　local　and　global　features.　Secondly,　the　efficient　channel　attention　module　is　added　to　strengthen　the　weights　of　effective　features　and　suppress　invalid　features　by　modeling　the　correlations　between　channels.　In　addition,　training　stability　is　achieved　through　the　use　of　the　WGAN-div　loss　function,　leading　to　the　generation　of　higher　quality　samples.　Finally,　the　proposed　algorithm　is　evaluated　through　qualitative　and　quantitative　comparisons　with　several　recent　methods　on　both　the　GOPRO　public　dataset　and　a　self-constructed　QR　code　dataset,　respectively.　The　experimental　results　demonstrate　that,　compared　to　the　other　methods,　the　proposed　algorithm　has　shown　significant　improvements　in　both　processing　time　and　recognition　accuracy　when　dealing　with　the　task　of　severe　motion-blurred　QR　code　recognition.