Abstract: A porosity prediction method based on selective ensemble learning is disclosed. On the basis of the typical machine learning method, the principal component analysis method is used to analyze data from support vector machines, radial basis functions. A group of excellent individual learning models are selected from the classical models such as RBF (RBF) neural network, random forest, ridge regression and K nearest neighbor regression to form the ensemble learning model. The weights of individuals in the ensemble model are obtained by the method of “principal component weight average” and the output of the ensemble learning model is finally obtained by the method of weighted average. The PCA-SEN model overcomes the shortcomings of a single model and has strong generalization ability. This method is used to predict reservoir porosity in order to get more accurate prediction results.