Abstract: The numerical renormalization group method based on tensor networks is widely used in physics, and has become an important member in the family of quantum manybody computational methods. In recent years, machine learning based on neural networks has entered the physical community, and has been successfully applied to quantum many- body systems. This review gives a brief survey about the applications of the two networks in condensed matter physics and statistical physics, and discusses their interplay and combinations.