This paper addresses the synchronization control problem of memristive neural networks in complex dynamic environments and proposes a preset-time synchronization control strategy based on the Lyapunov method. The proposed approach not only guarantees synchronization within a finite time but also allows the convergence time to be predetermined according to system requirements, thereby achieving both flexibility and efficiency in theoretical analysis and practical implementation. The paper first reviews the background and applications of memristive neural networks in intelligent computation, then analyzes the limitations of conventional synchronization control strategies. On this basis, a novel controller design method is developed. Rigorous theoretical derivations and stability proofs are presented to establish sufficient conditions for the effectiveness of the proposed method. Numerical simulation results verify the theoretical findings and demonstrate that the proposed strategy exhibits significant advantages in convergence speed, robustness, and disturbance rejection.