For the sensitive data streams with high-dimensional redundancy in heterogeneous complex information networks, the probability of data feature formation is low, which leads to multiple mining, high memory usage, low mining accuracy and long running time. Aiming at the above problems, a dynamic network sensitive data stream mining method based on the maximum inter-class divergence is proposed. The maximum difference interval between sensitive data is used as the basis for classification to obtain the maximum inter-class divergence of the network sensitive data. The optimal divergence iterative function is determined in the genetic iterative state. The mining characteristics of the iterative function are preferably selected to obtain the dynamic mining characteristics. Clustering analysis is performed on the mining characteristics to obtain data hiding information modes. These modes are evaluated, and knowledge representation is carried out on the reasonable information modes, so as to realize the dynamic mining of the sensitive data streams in the heterogeneous complex information networks. The experimental results show that the mineable feature formation probability of the method can be up to 98%, and the labels are close to the actual values. The method has the advantages of high mining accuracy, short running time and low memory usage.