The invention provides a new missing database data completion method. The method is characterized by comprising steps: 1, missing detection is carried out on a given data set; 2, dimension reduction of an input variable is carried out, correlation between input dimensions is analyzed, pivoting (PCA) is adopted to select a correlated input dimension, and a new input data set is formed; 3, training set k partitioning is carried out, a cluster (Kmeans) is used for carrying out partitioning on the input training set, and k classes of training sets are obtained; 4, a k plane regression function is built, the optimal regression coefficient and the geometric center of each plane are solved, and a regression fitting function is given; and finally, data completion test is carried out. The experiment proves that the data completion method is extremely effective; in an allowable error range, a completed database with a use value is obtained; and the challenging technical problem brought to machine learning and data mining due to data incompletion can be solved to a certain degree; and the big data application technology progress is pushed.