Traditional radar emitter identification suffers from low accuracy, bad real-time performance and low robustness under the condition of low SNR. To solve the problem, a radar emitter identification algorithm based on random forest is proposed. This algorithm takes Carrier Frequency (CF), Pulse Width (PW) and Pulse Recurrence Interval (PRI) as features of identification. Firstly, random sampling is conducted on the priori sample set to obtain multiple training sets. Secondly, the training sets are used to build a number of decision tree classifiers. Finally, the decision tree classifiers are used to identify new features and the final identification results are obtained by voting. Simulation results show that this algorithm has good robustness and real-time performance even under low SNR, which can effectively identify radar emitter on the battlefield.