Abstract: Abstract: Aiming at the problem that the traditional pooling algorithm in Convolutional Neural Networks cannot well consider the correlation between each element in the pooling domain and the features contained in the pooling domain, a pooling algorithm based on Gaussian function was proposed. Firstly, according to the value of each element in the pooling domain and the maximum value of all elements, the three parameter values of the Gaussian function was calculated by the algorithm. Then the Gaussian function was used to calculate the weight of all elements in the pooling domain. Finally, the weighted average value of all elements in the pooling domain was calculated according to the weight, which was used as the pooled result. LeNet5, VGG(Visual Geometry Group)16, ResNet(Residual Network)18 and MobileNet v3 were selected as the experimental models. and experiments were carried out on public data sets CIFAR-10, Fer2013 and the German Traffic Sign Recognition Benchmark (GTSRB). Max pooling, average pooling, random pooling, mix pooling, fuzzy pooling, fusion random pooling and soft pooling were compared in the experiments. The results show that the accuracy by 0.5 to 6 percentage points is improved by the algorithm compared with other algorithms on the three data sets, and the running time of the algorithm is better than the other pooling algorithms except the max pooling algorithm and average pooling algorithm, so as to verify that the algorithm is effective and suitable for the situation where the operation time is not high but the accuracy is high.