In order to solve the problems in classification, improve the classification accuracy and generalization ability of a single classifier, based on the degree of determination of factor space theory, a causal tree algorithm is proposed; Ensemble multiple causal trees, provide a definition of factor importance, obtain advantage factors, set importance thresholds in numerical experiments to reduce the set of condition factors, randomly select factors and then obtain the causal forest algorithm. Experimental comparison was conducted on the classification datasets of the UCI database to comprehensively evaluate the causal tree algorithm, causal forest algorithm, SVM algorithm and random forest algorithm. The experimental results showed that the causal forest algorithm performed well in terms of accuracy, precision, recall, F1 value, and AUC indexes, especially on the Vote and Cancer datasets, with good predictive and generalization abilities. The research conclusion expands the theoretical and applied research of factor space in data mining.