Network size is one of the fundamental information of distributed applications. The approach to estimate network size must feature both high accuracy and robustness in order to adapt to the dynamic environment in different topologies. However, existing approaches fail to guarantee accuracy and robustness simultaneously in dynamic topologies due to the randomness of nodes sampled. In this paper, we propose a churn adaptive approach to network size estimation – CANSE, which collects closest nodes in identification to each node’s identification by sampling nodes periodically. Each node collects closest identifications by two schemes. One scheme is sampling random nodes from random walks along the topology. The other one is exchanging the closest identifications with other nodes. Finally, each node calculates the average spacing of the closest identifications collected to estimate network size. Compared with existing approaches, extensive experiments show that CANSE provides accurate estimation values quickly in various dynamic topologies.