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Current methods of service composition in cloud manufacturing study only service composition problems from one angle,it is insufficient to considerate service composition in cloud manufacturing based on multi-objective transactions with low service composition quality.To achieve agile, intelligent and stable service composition in cloud manufacturing, with studying general cloud pattern analysis on multi-objective transactions,general cloud pattern expression on fuzzy correlation features of multi-objective transactions, algorithms on fuzzy correlation clustering of multi-objective transactions of service composition in cloud manufacturing,reverse learning algorithms, alternative service recommendation algorithms, trigonometric fuzzy function and undominated ranking genetic formula were improved,and a agile,intelligent and stable algorithm on service composition in cloud manufacturing was designed. Finally,validating experiments were implemented,and performance of this proposed algorithm was analized compared with traditional algorithms.Experimental results showed this algorithm had shorter composition response time, less error and higher astringency,agility,intelligence, dynamic evolution,stability than those of traditional algorithms.Therefore, this proposed algorithm achieves effective service composition in cloud manufacturing based on fuzzy correlation clustering on multi-objective transactions,it has higher utility.