MapReduce　framework　of　cloud　computing　has　an　effective　way　to　achieve　massive　text　categorization.　In　this　paper　a　distributed　parallel　text　training　algorithm　in　cloud　computing　environment　based　on　multi-class　Support　Vector　Machines(SVM)　is　designed.　In　cloud　computing　environment　Map　tasks　realize　distributing　various　types　of　samples　and　Reduce　tasks　realize　the　specific　SVM　training.　Experimental　results　show　that　the　execution　time　of　text　training　decreases　with　the　number　of　Reduce　tasks　increasing.　Also　a　parallel　text　classifying　based　on　cloud　computing　is　designed　and　implemented,　which　classify　the　unknown　type　texts.　Experimental　results　show　that　the　speed　of　text　classifying　increases　with　the　number　of　Map　tasks　increasing.　©　(2013)　Trans　Tech　Publications,　Switzerland.