An individual identification method of communication radiation sources based on Power Spectral Density(PSD) fingerprint characteristics and intelligent classifier is proposed in order to prevent the occurrence of problems such as device cloning, replay attacks and user identity impersonation, and to accurately identify and authenticate Internet of Things(IoT) objects. First, the radio frequency baseband signal is collected by receiver, and the in-phase signal is collected. Then the steady-state signal segment is intercepted through variance trajectory detection, and data normalization processing on the steady-state signal segment is performed; the PSD of the steady-state signal segment is calculated after data normalization processing to obtain a feature vector, and the feature vector is used as the radio frequency fingerprint of the transmitter. Finally, an intelligent classifier is adopted to identify the radio frequency fingerprint to complete the individual identification of the communication radiation source. The experimental test to identify eight wireless data transmission radio E90-DTU devices and 100 WiFi network card devices of the same manufacturer, the same type and the same batch shows that the proposed method can obtain good recognition accuracy when applied in Line-Of-Sight(LOS) scenarios, mixed scenes of LOS and Non-line-Of-Sight(NOS) scenarios, low signal-to-noise ratio scenes, and scenarios with a large number of IoT devices, etc.