A method of target recognition based on the blur-invariant convolutional neural network (BICNN) model is proposed. The BICNN model introduces a new blur-invariant layer, which is different from the traditional convolutional neural network (CNN )models. BICNN is trained by the adding of the blur-invariant constraint term and the regularization to optimize a blur-invariant objective function. The value of the fuzzy invariant objective function is reduced to make the training samples consistent with the feature maps before and after the blurring, and thus the blur invariance is achieved finally. The test results show that BICNN can solve the problem of a low recognition rate caused by blur and improve the recognition rate of the motion blurred images.