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Traditional optimization methods usually perform aerodynamic design by manipulating geometry shapes, which would ignore useful information in flowfields. In order to improve the defect, a novel pressure-based optimization (PBO) method is developed in this study to conduct the design of airfoils using deep learning techniques. In this method, a one-dimensional convolutional auto-encoder (1D-CAE) is employed to extract representative features from the pressure distributions. With these latent features as inputs, two multilayer perceptrons are constructed to predict airfoil profiles and forces, respectively. Furthermore, these deep learning models are combined with the genetic algorithm to minimize the drag of the RAE2822 airfoil. Finally, two data mining techniques are implemented to investigate the optimization mechanisms. Results show that, after the latent dimension reaches 12, the reconstructed pressure distributions by 1D-CAE agree well with the original ones. The predicted forces are close to the observations, with the mean absolute relative error below 0.21% for CL and 0.69% for CD on the testing set. Meanwhile, the predicted airfoils show a good match with the true profiles, and the root mean square error is less than 0.08% for all the testing samples. Optimized results show that, compared with several state-of-the-art optimization approaches, the proposed PBO method achieves a lower drag coefficient and has the potential to provide better airfoil designs. In addition, the discovered mechanisms indicate that the pressure distributions could be manipulated by varying pressure features to achieve drag reduction of airfoils. [ABSTRACT FROM AUTHOR]