Complication prediction is a typical multi-label classification problem.Making effective use of the correlation between labels is critical to improve the accuracy of multi-label classification models. In response to this problem, a predictive model AR-MLKNN (multi-label k-nearest neighbor based on association rules) of common complications in hypertension patients was proposed. Firstly, The characteristic representation of patients was constructed from clinical concepts in different semantic spaces.Secondly, the association information of disease labels was used to quantify the concurrent relationship of complications, and the membership probability of each disease label was calculated based on the probability distribution of k-neighborhood labels of unknown samples. Finally, the complication concurrency relationship and disease label membership probability were mapped to form a complication risk matrix. Based on the complication risk value, the classification threshold is dynamically adjusted based on the principle of minimizing the classification loss to obtain the optimal classification result. The experimental results show that the model can predict hypertension complications more accurately. The F1 -score reaches 82%, which is 8% higher than the conventional ML-KNN(multi-label k-nearest neighbor) model. Therefore it may have some application value in the field of clinical -assisted decision.