To solve the problem that Non Line Of Sight (NLOS) state identification requires classification of known channel types, a channel environment classification algorithm based on Convolutional Neural Network (CNN) was proposed. Firstly, an Ultra-WideBand (UWB) channel was sampled, and a sample set was constructed. Then, a CNN was trained by the sample set to extract features of different channel scenes. Finally, the classification of UWB channel environment was realized. The experimental results show that the overall accuracy of the model using the proposed algorithm is about 93.40% and the algorithm can effectively realize the classification of channel environments.