Deep convolutional neural networks are generally large in scale and complex in computation, which limits their application in high real-time and resource-constrained environments. Therefore, it is necessary to optimize the compression and acceleration of the existing structures of convolutional neural networks. In order to solve this problem, a hybrid compression method combining pruning and stream merging was proposed. In the method， the model was decompressed through different angles, further reducing the memory consumption and time consumption caused by parameter redundancy and structural redundancy. Firstly, the redundant parameters in each layer were cut off from the inside of the model. Then the non-essential layers were merged with the important layers from the structure of the model. Finally, the accuracy of the model was restored by retraining. The experimental results on the MNIST dataset show that the proposed hybrid compression method compresses LeNet-5 to 1/20 and improves its running speed by 8 times without reducing the accuracy of the model.