We constructed a Bayesian quantile linear regression model based on Gibbs-DA sampling algorithm for the uncertainty risks of quintile regression model parameters.According to the normal-exponential representation property of asymmetric Laplace distribution, we established a working likelihood function for the quantile regression model with latent variables, gave its parameters' posterior distribution with a multivariate prior distribution, whose full condition distribution is generalized Guassian.We also used Gibbs sampling technique and data argumentation method to design a Gibbs-DA simulation procedure.Finally, we made an empirical study to analyze energy consumption in China.The results have shown that Bayesian procedure can be efficiently used to build quantile regression models and applied to the elasticity of energy consumption.