Aiming at the problem of modeling and analysis under high-dimensional data a robust estimation method based on elastic network method and composite quantile regression was proposed. In this estimation method the proposed model can effectively perform variable selection and coefficient compression and deal with multicollinearity and group effects between data and has wide adaptability in the era of big data. At the same time compared with the existing penalized least squares estimation and penalized quantile regression estimation this estimation method not only relaxes the distribution requirements of the model error term but also comprehensively considers the loss of multiple quantiles which can maintain stronger robustness and anti-interference in the face of outliers or data with spiky thick-tailed distributions. Under certain conditions a theoretical analysis of the consistency and sparsity of the constructed model estimates is carried out. The results show that the proposed model can completely compress uncorrelated variables to zero and the estimate and the true coefficient have the same probability of tending to 1. In addition in terms of numerical simulation five kinds of error term distribution conditions are set. According to the four indicators set the comparison with other penalty function models and loss function models is carried out. The results show that the newly proposed method has better robustness and effectiveness.