GoogLeNet contains a number of parallel convolutional layers and pooled layers, which makes the network highly expressive and leads to redundant and computational quantity of GoogLeNet parameters. The fundamental way to solve the problem is to rarefly network. A pruning algorithm consists of three steps: training the network, pruning the low-weight connection, and retraining the network, retaining the strong correlation between the convolution layer and the fully connected layer. It reduces the number of network structure and parameters to obtain an approximate network model and does not affect the accuracy of the network posterior probability estimation in order to achieve compression. The traditional calculation methods are not suitable for the non-uniform and sparse data structures. The threshold pruning algorithm is proposed, which sets a suitable threshold and reduces the original 10.4 million parameters in the original GoogLeNet model to 650,000, approximately 16 times compressed. After pruning the original network, the accuracy rate will be reduced. Then the accuracy of the network is comparable with the original model through a few iterations, and the effect of the compressed mode is achieved. It is proved that the proposed threshold pruning algorithm can effectively improve the GoogLeNet model training process.