Existing incomplete data filling algorithm are all use the same method to fill all the missing values

and did not consider the importance of each value

thus

makes all algorithms low efficiency and poor real-time. Therefore

this paper proposes a new data filling algorithm based on distinguishing the importance of attributes

it uses attribute reduction to distinguish important attributes and unimportant attributes

then

uses the improved mahalanobis-based algorithm to imputing the missing value that belong to the important attributes

and unimportant missing values according to the similarity -probabilistic method

thus

ensure that the accuracy of data

at the same time

make sure the real -time and practicality.at last

the experimental part using the Digital-home system and the UCI standard datasets to analysis the algorithm performance

verifying the superiority of the algorithm.