To deal with the probability learning problem in Multi-Function Radar (MFR) based on Stochastic Context-Free Grammar (SCFG) model, a new fast learning algorithm of grammatical probabilities in MFR based on Earley algorithm was presented on the basis of traditional Inside-Outside (IO) algorithm and Viterbi-Score (VS) algorithm. The intercepted radar data was pre-processed to construct an Earley parsing chart which can describe the derivation process. Furthermore, the best parsing tree was extracted from the parsing chart based on the criterion of maximum sub-tree probabilities. The modified IO algorithm and modified VS algorithm were utilized to realize the learning of grammatical probabilities and MFR parameter estimation. After getting the grammatical parameters, the state of MFR was estimated by Viterbi algorithm. Theoretical analysis and simulation results show that compared to the conventional IO algorithm and VS algorithm, the modified algorithm can effectively reduce the computation complexity and running time while keeping the same level of estimation accuracy, which validates that the grammatical probability learning speed can be improved with the proposed method.