In order to improve the caching performance in information centric networks, an adaptive caching strategy based on concept drifting learning (CDL) was proposed. Considering the supplementary action of the node data and content data on improving caching performance, firstly, the status data flow of nodes and content were used as network resources, and then the mapping relationship, namely concept, between the multidimensional state attribution data based on the status data flow and the matching relationship value was mined. Finally, utilizing this mapping function, a matching algorithm to predict the matching relationship between the node and the content in the next time period was proposed. In order to improve the accuracy of the matching algorithm, a concept drifting detection algorithm based on information entropy was proposed. When the concept drifting of the state attribution data by the information entropy was captured, a new mapping relationship was learning by the proposed recurring concept caching algorithm. Simulation results show that CDL outperforms CEE, LCD, Prob, and OPP when looking at cost reduction of network operation and enhancement in quality of user experience.