Quadratic programming methods have been widely used to solve the redundancy problem of control manipulators due to their ability to optimize performance indicators under physical constraints. However, conventional quadratic programming methods require known parameters. In practical work environments, the physical parameters of the manipulator can be uncertain. Therefore, an adaptive control method that can synchronize parameter identification and network control is necessary. We propose an adaptive varying parameter projection neural network method that begins from the perspective of joint velocity space. This method decouples the unknown parameters and nonlinear parts that require identification in the Jacobian matrix of the manipulator. For the first time, a projection network with varying parameters is introduced for redundant decomposition, greatly improving the accuracy of trajectory tracking. Additionally, we design an iterative identification equation based on neural dynamics and introduce position feedback, making Jacobian matrix identification more accurate. The proposed method can solve the problem of redundant resolution of robotic arms under actual physical constraints and unknown physical parameters and can improve performance indicators. Theoretical analysis and simulation results demonstrate the feasibility and performance of the proposed method.