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.