Abstract: In view of at the inter symbol interference(ISI) caused by serious multipath effect in underwater acoustic communication, a blind decision feedback equalizer based on quasi Newton optimization neural network (named as B-QNBPDFE) is proposed, in which the structure of blind decision feedback equalizer (B-DFE) and back propagation (BP) neural network are combined, and the convergence speed of neural network is improved by quasi Newton algorithm. Two single hidden layer BP networks are used to complete the function of DFE feedforward and feedback filters. The weights of neural networks are calculated by quasi Newton iteration. Without calculating the second derivative, the inverse matrix of Hessian matrix is approximated by approximate matrix. The iterative calculation is carried out by measuring the gradient change of weights of each layer. Finally, phase correction is carried out by phase-locked loop. The simulation results show that the blind decision feedback equalizer based on Quasi Newton optimization neural network has faster convergence speed and lower bit error rate in underwater acoustic channel equalization.