Quantile regression estimates conditional quantiles and has wide applications in the real world. Estimating high conditional quantiles is an important problem. The regular quantile regression (QR) method often sets a linear or non-linear model, then estimates the coefficients to obtain the estimated conditional quantile. This approach may be restricted by the model setting. To overcome this problem, this paper proposes a direct nonparametric quantile regression method. The asymptotic properties of this direct estimator are given. Monte Carlo simulations show good efficiency for the proposed direct nonparametric QR estimator relative to the regular QR estimator. The paper also investigates a real-world example of applications by using the proposed method. Comparisons of the proposed method and existing methods are given.