Currently, edge detection is an effective means of collecting and analyzing various diseases information from mural collections by using this and data mining based on digital orthophoto map (DOM). But it is hard to extract better edges of mural diseases with traditional edge detection algorithms. Therefore, a new K-means Sobel algorithm is proposed and two evaluation factors are given to judge the extracting effect. Experiment results demonstrate that we can get a better effect by using new method than traditional algorithms. At last, vectorizing detected results, we can gain diseases areas. On that basis, a decision tree about mural diseases severities is established to provide useful information for mural diseases investigation and repair.