Aimed at the fact that data samples in the same data set are difficult to recognize because of maldistribution, this paper proposes a fast radar emitter recognition algorithm based on rough K-means combined with AdaBoost. The algorithm is composed of two stages. At the primary recognition stage， an improved rough kmeans algorithm is proposed, and the data feature space is divided into the certain area, the rough area and the uncertain area to construct a fast radar emitter recognition algorithm model so as to filter and recognize the data set. And at the same time a heuristic approach is proposed to solve the inherent shortcomings of the original rough K-means by ascertaining its initial clustering number and centers. And at the advanced recognition stage, unknown samples dwelling in the uncertain area are recognized by the multiclass AdaBoost classifier trained by the unknown ones in the rough area, thus promoting the recognition accuracy of the algorithm. The simulation results show that compared to RBFSVM and AdaBoost, the scope of an accuracy fluctuation is from -0.1% to +1.4%, the shrinkage of a training time is 0.857 s, and the shrinkage of a test time is 0.005 s at most, and apparently the computational complexity is lowered and the time consumed is shortened respectively by using this new algorithm under conditions of reserving comparatively high recognition accuracy and generalization capability. By so doing, this provides fast radar emitter recognition algorithmsdesigning with new train of thought.