According to the shape features of wind shear images extracted by wavelet invariant moment based on cubic B-spline wavelet basis, an improved Genetic Algorithm (GA) was proposed to apply to the type recognition of microburst, low-level jet stream, side wind shear and tailwind-or-headwind shear. In the improved algorithm, the adaptive crossover probability only considered the number of generation and mutation probability just emphasized the fitness valve of individuals and group, so that it could control the evolution direction uniformly, and greatly maintain the population diversity simultaneously. Lastly, the best feature subset chosen by the improved genetic algorithm was fed into 3-nearest neighbor classifier to classify. The experimental results show that it has a good direction and be able to rapidly converge to the global optimal solution, and then steadily chooses the critical feature subset in order to obtain a better performance of wind shear recognition that the mean recognition rate can reach more than 97% at last.