Purpose: Mapping the Minnesota Living with Heart Failure Questionnaire (MLHFQ) to SF-6Dv2 in Chinese patients with chronic heart failure, and to obtain the health utility value for health economic assessment. Methods: Four statistical algorithms, including ordinary least square method (OLS),Tobit model, robust MM estimator (MM) and censored least absolute deviations (CLAD), were used to establish the alternative model. Models were validated by using a tenfold cross-validation technique. The mean absolute error (MAE) and root mean square error (RMSE) were used to evaluate the prediction performance of the model. The Spearman correlation coefficient and Intraclass Correlation Coefficients (ICC) were used to examine the relationship between the predicted and observed SF-6Dv2 values. Results: A total of 195 patients with chronic heart failure were recruited from 3 general hospitals in Beijing. The MLHFQ summary score and domain scores of the study sample were negatively correlated with SF-6Dv2 health utility value. The OLS regression model established based on the MLHFQ domain scores was the optimal fitting model and the predicted value was highly positively correlated with the observed value. Conclusion: The MLHFQ can be mapped to SF-6Dv2 by OLS, which can be used for health economic assessment of cardiovascular diseases such as chronic heart failure.