In order to achieve accurate dynamic model identification of the hyper-redundant manipulator， a semiparametric dynamic model identification method based on iterative optimization and neural network compensation was proposed. First， the dynamic model of the hyper-redundant manipulator and the base parameter set were introduced， joint nonlinear friction model was established， and the excitation trajectory was generated using genetic algorithm to optimize the condition number of the regression matrix. Second， the physical feasibility constraint of the manipulator dynamic model was established， and a two loops identification network was designed to identify the inertial parameters and joint friction model of the hyper-redundant manipulator based on the iterative optimization method. Finally， the BP neural networks were trained to obtain the semiparametric dynamic model of the hyper-redundant manipulator by using data set. A series of identification algorithms were compared and analyzed. The experimental results show that， compared with the traditional least squares algorithm and weighted least squares algorithm， the identification algorithm proposed in this paper can improve the sum of identify torque residual root mean square （RMS） of joints by 32.81% and 23.76%， respectively. The sum of torque residuals of the semi-parametric dynamic model is 23.56% higher than that of the full-parametric dynamic model. The identification results verify the effectiveness of the proposed identification method.