Since Convolutional Neural Network （CNN） conversion method based on the weight normalization method for event stream data has a large loss of accuracy and the effective deployment of floating-point networks is difficult on hardware， a network conversion method for dynamic event stream was proposed. Firstly， the event stream data was reconstructed as the input of CNN for training. In the training process， the quantized activation function was adopted to reduce the accuracy loss， and a symmetric fixed-point quantization method was used to reduce the parameter storage. Then， instead of equivalence principle， pulse count equivalence principle was used to adapt to the sparsity of data better. Experimental results show that on three datasets N-MNIST， POKER-DVS and MNIST-DVS， compared with using the traditional activation function， Spiking Convolutional Neural Network （SCNN） using the quantized activation function has the recognition accuracy improved by 0.29 percentage points， 8.52 percentage points and 3.95 percentage points respectively， and the conversion loss reduced by 21.77%， 100.00% and 92.48% respectively. Meanwhile， the proposed quantized SCNN can effectively save 75% of storage space compared with high-precision SCNN generated on the basis of the weight normalization method， and has the conversion loss on N-MNIST and MNIST-DVS datasets reduced by 6.79% and 46.29% respectively.