The radio frequency fingerprints are inherent features of the device hardware, and will not change with the transmitted signal, therefore they are often used in communication anti-spoofing. In this paper, the neural network is adopted to process the original signal samples obtained by the receiver, including I/Q sequence, amplitude/phase, binary image of constellation diagram and color density diagram of constellation diagram to achieve anti-deception effect. When the signal-to-interference and noise ratio is in the range of -30 dB to 30 dB, the signal recognition accuracy can reach up to 99.93%. Being different from the existing literature, the method can be adapted to the scenes with different signal-to-interference and noise ratios. This research shows that the proposed method is feasible to achieve anti-spoofing in a complex communication environment where spoofing signals and legal signals coexist.