In　the　aerodynamic　design,　optimization　of　the　pressure　distribution　of　airfoils　is　crucial　for　the　aerodynamic　components.　Conventionally,　the　pressure　distribution　is　solved　by　computational　fluid　dynamics,　which　is　a　time-consuming　task.　Surrogate　modeling　can　leverage　such　expense　to　some　extent,　but　it　needs　careful　shape　parameterization　schemes　for　airfoils.　As　an　alternative,　deep　learning　approximates　inputs-outputs　mapping　without　solving　the　efficiency-expensive　physical　equations　and　avoids　the　limitations　of　particular　parameterization　methods.　Therefore,　this　paper　presents　a　data-driven　approach　for　predicting　the　pressure　distribution　over　airfoils　based　on　Convolutional　Neural　Network　(CNN).　Given　the　airfoil　geometry,　a　supervised　learning　problem　is　presented　for　predicting　aerodynamic　performance.　Furthermore,　we　utilize　a　universal　and　flexible　parametrization　method　called　Signed　Distance　Function　to　improve　the　performances　of　CNN.　Given　the　unseen　airfoils　from　the　validation　dataset　to　the　trained　model,　our　model　achieves　predicting　the　pressure　coefficient　in　seconds,　with　a　less　than　2%　mean　square　error.　©　2020　Elsevier　Masson　SAS