Abstract: Wireless channel scene identification is of great significance for wireless resource scheduling and system performance optimization. Based on the QuaDriGa platform, this paper studies the application of back propagation neural network(BPNN) and convolutional neural networks(CNN) in wireless channel scene recognition. Firstly, based on QuaDriGa, the channel impulse response (CIR) in different scenarios is generated, and channel parameters such as delay spread and angle spread are extracted. In this paper, BPNN is used to train the parameters of different scenes. For CNN, it is necessary to convert the one-dimensional CIR into a two-dimensional image and then train it through operations such as “tap movement, order of magnitude fine-tuning, and autocorrelation”. Finally, the recognition accuracy is calculated and the K-fold cross-validation is used to verify the generalization ability of the two models. The results show that CNN has higher recognition accuracy than BPNN, but BPNN has higher recognition efficiency. Both can be used for intelligent sensing and recognition of future communication network scenarios.