To make full use of maintainability multi-source prior data and improve the fusion accuracy of prior distributions to ensure the accuracy of maintainability verification results, a prior distribution determination method for maintainability multi-source conflict evidence data fusion is proposed to solve the problem of conflicts in multi-source data. Fully mine and extract the feature information of multi-source data, construct evidence mass functions based on sample size, distribution characteristics and data importance respectively, comprehensively consider the correlation between evidence and the uncertainty of evidence itself, introduce angle cosine to measure the degree of conflict between evidence, introduce information entropy to measure the uncertainty of evidence, and then combine the support and uncertainty of evidence to jointly modify evidence, establish a multi-source conflict evidence data fusion model to realize the effective fusion of multi-source data, and then determine the comprehensive prior distribution. Finally, two cases are analyzed to verify the effectiveness and feasibility of the proposed method.