This paper proposes a novel Forward–Backward Smoothing-Based Learning Subspace Method (FBSLSM), which can satisfy the equirements of being insensitive to the order of presentation of the training samples, and is of faster convergence speed. This method is applied to the recognition of simulating High Resolution Radar (HRR) targets (two for ships, one for chaff). Moreover, for recognition of HRR targets, a new selection method of subspace dimensionality is given. The computer simulating experiments show that the corresponding performance of proposed FBSLSM such as rate of correct recognition and convergence speed is better than that of the ALSM presented by Oja.