Most classification models in deep learning-based encrypted traffic classification methods have deep and straight structure with the problem of vanishing gradient， and the increase of the number of network layers leads to significant increase of model structure and computational complexity. Based on these， an encrypted traffic classification method based on improved Inception-ResNet was proposed. In the method， the classification model was constructed by improving the Inception module and embedding it into the convolutional neural network as a residual block in a residual structural connection way. In addition， the loss function of the classification model was improved， and the effectiveness of the proposed method was verified by using VPN-nonVPN dataset. Experimental results show that the proposed method achieves the precision， recall， and F1 score of more than 94.21%， 92.53%， and 93.31%， respectively， in the classification experiments of two senerios. In the comparison experiments with other methods， taking the 12-class classification experiment， which is the most difficult one， as an example， the proposed method is higher than C4.5 decision tree algorithm and 1D-CNN （1 Dimensional-Convolutional Neural Network） by 13.91 and 9.50 percentage points higher in precision and by 14.87 and 1.59 percentage points in recall. Compared with the algorithms such as CAE （Convolutional Auto Encoding） and SAE （Stacked Auto Encoder）， the proposed method not has obvious improvement on the indicators， but has significant shorter single training time， fully demonstrating that the proposed method is a state-of-the-art method.

Aiming at the problem that the traditional pooling algorithms in Convolutional Neural Network （CNN） 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 were calculated. Then， the Gaussian function was used to calculate the weights of all elements in the pooling domain. Finally， the weighted average value of all elements in the pooling domain was calculated according to these weights. Finally， the obtained value was used as the pooling result. LeNet5， VGG （Visual Geometry Group）16， ResNet （Residual Network）18 and MobileNet v3 were selected as the experimental models. Experiments were carried out on public datasets CIFAR-10， Fer2013 and German Traffic Sign Recognition Benchmark （GTSRB）， and max pooling， average pooling， random pooling， mixed pooling， fuzzy pooling， fused random pooling and soft pooling were selected to compare. Experimental results show that the proposed algorithm improves the accuracy by 0.5 percentage points to 6 percentage points compared with other algorithms on the three datasets， and the running efficiency of the proposed algorithm is higher than those of the other pooling algorithms except max pooling algorithm and average pooling algorithm， so as to verify that the proposed algorithm is effective and suitable for the situations where the operation time demand is not high but the accuracy demand is high.