The invention discloses a target detection method based on an evolutionary neural network under a constraint condition, and the method comprises the steps: constructing a plurality of structural blocks and a 
population composed of a plurality of individuals, and carrying out the coding of each individual through a variable-length coding mode, thereby completing the initialization of the 
population; performing training updating on each individual according to the training 
data set; evaluating the individuals on the 
verification data set, and calculating the accuracy and complexity of the individuals to obtain the fitness of the individuals; according to a preset constraint quantity, adjusting the individual fitness by using a 
constraint control method, and adjusting the individual framework of which the accuracy exceeds a threshold value; selecting male parents from the 
population according to the adjusted fitness, generating first-level 
offspring through male parent crossing, and generating second-level 
offspring through probabilistic variation of the first-level 
offspring; and selecting the parent, the first-level filial generation and the second-level filial generation to generate a 
new population, and performing iterative evolution. According to the invention, a light-weight structure unit is designed, a constraint method is utilized, and an optimized target detection result is achieved without artificial experience.