Abstract: A machine learning-based method for automatically determining abnormal points of a single indicator includes step 1: randomly selecting M sample points from training data as subsamples, and putting them into a root node of a tree; and step 2: randomly specifying a data dimension for projection, and randomly generating a cutting point p in data of a current node, where the cutting point is generated between a maximum value and a minimum value of the specified dimension in the data of the current node. The present disclosure optimizes conventional data analysis linear model functions and regression model functions, constructs a computer neural network in the algorithm, puts multiple perceptron parameters in a multi-layer network for learning and training, and adopts the principle of principal component analysis, to find out the abnormal data that violates the data correlation.