The openness of wireless media has been a security threat for traditional wireless network based on security protocol.While the Radio Frequency Fingerprint (RFF) identification is based on physical layer security,and considering the RFF is impossible to forge,the RFF identification can effectively improve the security of wireless network.Aiming at the multi-scene and multi-device identification,an RFF identification approach is constructed based on attention residual convolution neural network.The dataset contains 32 Wi-Fi modules,covering the 2.4 GHz module of 802.11b standard.The comparison results show that the recognition accuracy of the proposed approach is 90% for the 32 Wi-Fi modules,higher than that of traditional algorithm (86%) and convolutional neural network approach (89%);the recognition accuracy can be higher than 90% on the dataset with different sampling rates when the SNR is greater than 2 dB,which can reach as high as 96% when the SNR is greater than 20 dB.