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Radio frequency fingerprint identification (RFID) is a method of physical layer identity authentication. It is an important and basic research direction in electronic countermeasures, and plays an important role in providing information for modern warfare; In order to improve the accuracy of RFID in the complex environment of electronic warfare, and solve the problem that it is difficult to extract steady-state features due to the limited length of frequency hopping (FH) signal segments, an intelligent recognition technology based on multi dimension feature fusion of signals and deep convolution network feature extraction is proposed. The traditional constellation feature extraction method is improved, and the bispectrum, constellation and spectrum of Hilbert Huang transform (HHT) of signals are extracted for feature fusion, The effectiveness and robustness of this method are proved by setting up contrast experiments under different signal-to-noise ratio (SNR) and different input conditions; Compared with the traditional recognition method, this method has less computation and improves the recognition accuracy under various signal-to-noise ratios. The recognition accuracy of six PSK type FH radios in normal outdoor environment reaches 99.29%.