Wax deposition in oil wells seriously affects the normal production of oil wells during the development and production of oilfields. This phenomenon will block oil flow channels and reduce oil production during the production of oil wells. Wax deposition prediction in oil wells and advance maintenance of oil well equipment are pivotal to higher production capacity, lower maintenance cost and more intelligent management. To solve the problem of serious imbalance between the normal data and wax deposit data of oil wells, this study introduces two processing methods of non-equilibrium samples, ADASYN and ENN, which deal with the non-paraffin and paraffin samples separately. Finally, the random forest algorithm is used to integrate the training data set, and the intelligent AERF model is constructed to predict the wax deposition in oil wells. The experimental results show that the AERF model proposed in this study has a better prediction effect in the wax deposition data set of oil wells, greatly improving the prediction accuracy.