In order to distinguish the importance of different high-order crossover features and eliminate redundant cross features,and improve the accuracy of click through rate,a deep cross attention prediction network (DAPN) algorithm is proposed.DAPN represents the input information with sparse high-dimensional features as low-dimensional dense vectors and sends them to the factorization machine (FM) and deep cross attention (DCA) layer respectively.FM uses first-order features and second-order features to cross mine low-order cross features that never appear or rarely appear in training data.DCA layer uses scaled dot-product attention mechanism (DP_Att) to design cross attention layer,distinguish the importance of high-order cross features,and design the deep neural network (DNN) to model the high-order cross features.Simulation results show that DAPN has good prediction performance on public data sets,such as Movielens-1m and Avazu.DAPN uses parallel structure to effectively learn both low-order and high-order crossover features and improve estimation accuracy.