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Aiming at the problems of low detection accuracy and high false alarm rate of traditional intrusion detection algorithm, a network intrusion detection model combining batch normalization and deep neural network is proposed. Firstly, a batch normalization layer is added to the hidden layer of the deep neural network to optimize the output of the hidden layer, and then the adaptive gradient descent optimization algorithm of Adam is used to optimize the parameters of BNDNN automatically to improve the detection ability of the model. The simulation experiment with NSL-KDD data set shows that the detection effect of the model is better than shallow neural network (SNN), k-NearestNeighbor (KNN), deep neural network(DNN) and other detection methods; The overall detection rate is 99.41%, and the overall false alarm rate is 0.59%, which proves the feasibility of the model.