关键词: 重权关联因子, 可信度联想, 加权不确定推理, 疾病诊断, 可信度合成

Abstract: Constructing and using intelligent computer systems to assist human experts to solve problems of disease diagnosis about people and livestock is one of hot spots in the field of medical artificial intelligence research.Traditional diagnosis systems taking no consideration of timing and associability between clinical characteristics are always with the high rate of misdiagnosis，and lack of practicality.At the aim to improve the efficiency of machinery-diagnosis，in case of goats，the clinical relevance of the disease is analyzed，and mechanisms of associated factor into the method of Certainty-Factor-knowledge-representation is indroduced；and the CF-value of associate-factors with big weights is predicted through the channel of forecast by lowest-value，highest-value，weighted-value.Experimental results show that this method can significantly the improve inference rule activation rate and reduce the rate of misdiagnosis.

Key words: bigger-weighted-related, Certainty Factor forecast, weighted uncertainty reason, disease diagnosis, Certainty Factor Integration