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As a third-generation neural network, spiking neural network can overcome many problems in artificial neural networks, such as high power consumption and poor robustness. Transforming the pre-trained artificial neural network model is one of the main methods to obtain the deep spiking neural network model, but the spiking neural network obtained by this method has a high latency and cannot meet the real-time requirements. On the basis of the double threshold conversion method, the threshold balance technology is used to optimize the conversion process, and through theoretical derivation, a symmetric threshold LeakyReLU activation function is proposed, and the conversion process from artificial neural network to spiking neural network is sorted out. In addition, the leakage mechanism is used to optimize the structure of the transformed spiking neural network model, and the structure is trained by the spike-timing-dependent plasticity learning rule. Finally, experiments were carried out on the MNIST dataset and the CIFAR-10 dataset, and the results showed that the convergence speed and robustness of the optimized spiking neural network were greatly improved.