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In this paper, a fixed-time sliding mode position synchronization controller based on neural network is proposed for multi-manipulator system with unknown dynamics model. Firstly, a fixed-time terminal sliding mode surface and controller are designed based on the adjacent cross-coupled synchronization control strategy to ensure that the tracking error and synchronization error converge in a fixed-time, and the upper bound of the convergence time is independent of the initial state. Secondly, the weight updating law of RBF neural network is designed to estimate the unknown nonlinear dynamic model of the system. This method does not need prior knowledge of the parameters of the system model. The Lyapunov function is used to prove the fixed-time convergence and stability of the system. Finally, simulation results verify the effectiveness of the proposed method.