Target recognition based on wavelet transform and artificial neural network is an important research direction of image processing. However, gradient descent rules used by such methods tend to produce local minima. In order to solve this problem, a highly robust target recognition algorithm based on swarm intelligence algorithm was proposed, which can be effectively applied to various image recognition tasks such as volleyball target recognition. Firstly, the image was preprocessed and transformed into HSV space for background segmentation, and the features were extracted by wavelet moment invariants. Then using the new swarm intelligence algorithmwolf pack algorithm, the target image recognition based on wavelet neural network was optimized to improve global convergence and robustness. Simulation results show that compared with the original method, the proposed optimization method has higher recognition accuracy and better stability.