Deep blurring is a kind of expression feature of blurred infrared images. The accurate extraction of the deeply blurred region in infrared images is the foundation of extracting blurry infrared targets. On the basis of the excellent characteristics of recognition, learning, memory, tolerance and coordination exhibited by biological immune systems in antigen detection, extraction and elimination, a deeply blurred infrared target extraction algorithm based on optimal immune field neural immune network is proposed by combining the relationship between the nervous system and the immune system in biological immunity. The algorithm can provide a guiding role for the immune network in target and background classification of blurred infrared images by designing a neural network. By relying on the function of prior knowledge of neural network independent of the immune system, an optimal immune field neural immune network is designed and accurate extraction of blurred infrared targets is implemented. The experimental results show that the algorithm can extract targets in blurred infrared target images more effectively and accurately than other traditional target extraction algorithms for blurred infrared target images.