Constructs and verifies a recency-frequency (RF) model for predicting the loss to follow-up (LTFU) in acquired immunodeficiency syndrome (AIDS) patients in China. We exported the full data of AIDS outpatients in the research unit from August 2009 to September 2020 as the observation dataset, from September to December 2020 as the prediction dataset. The data cleaned to obtain one data element per person. The classic recency-frequency-monetary (RFM) model was expanded into RFM, RF, RFL and RFML models (L: length of treatment cycle), using the k-means for model construction and C5.0 for verification to the best predictive model. The best predictive model was subjected to two rounds of k-means clustering. In the retained data were randomly divided into a training set (70%) and a testing set (30%). The artificial neural network algorithm was used to predict the LTFU of AIDS patients and the confusion matrix was used to evaluate the performance. The observation dataset included 16,949 elements and the prediction dataset had 10,748 elements. In the best predictive model, an RF model with a quality of 0.82. A retained of 13,799 data elements were retrained and randomly binned into a test set and a verification set, the accuracy rate were100.0%. LTFU of AIDS patients was predicted in the prediction set with a correct rate of 99.89% and the accuracy and precision were 85.41% and 99.76%, respectively. The RF model verified using a neural network algorithm is effective, reliable and feasible to predict the LTFU of AIDS patients in China.