Existing fog detection models have defects and the optimization of deep neural network is difficult. Based on imitating human cognitive model, transfer learning and closedloop control theory, this paper explores an intelligent cognitive method of fog level based on dynamic interleaved group convolution. Firstly, an interleaved group convolution layer is constructed to reduce the channel redundancy of convolution operation. Secondly, a discriminability measure index and a cognitive decision information system are constructed to obtain the spatial data structure of differentiated and simplified features of fog images. Thirdly, the deep stochastic conguration networks classifier is designed and the classification criterion with strong generalization ability is constructed. Finally, based on the generalized error and entropy theory, the cognitive model that mimes human repeatedly deliberates and compares to evaluate the credibility of the cognitive results of fog grades in real time. Based on the transfer learning mechanism, the selfoptimization reconstruction of the multilevel differentiated feature space of fog images and their classification criteria are realized, and the lowcredibility fog images are rerecognized again. The average recognition rate of 15,000 fog images is 9598%. The experimental results show that compared with other algorithms, this method enhances the generalization ability and improves the cognitive accuracy of the model.