The article introduces a new method called quantile function regression for analyzing high-dimensional data. This method helps estimate coefficients more stably across different quantile levels, allowing for better variable selection. By using a B-spline model, the estimated conditional quantile becomes continuous, leading to more accurate results. The proposed method shows good performance in both simulation studies and real data applications, with consistent variable selection and a reliable rate of convergence.