By automatically designing neural network structures， Neural Architecture Search （NAS） overcomes the trial-and-error limitations of traditional manual design， thereby substantially reducing the reliance of deep learning model development on professional experience， while enhancing the model performance significantly. Among various methods， Differentiable Neural Architecture Search （DNAS）， which leverages the one-shot framework and gradient descent optimization， has drastically shortened the search time and has emerged as a main research direction in current research. However， a systematic review of differentiable methods and their developmental trajectories is still absent. To bridge this gap and provide a reference for related studies， recent DNAS methods were synthesized and analyzed. First， the fundamental theories and benchmark datasets of DNAS algorithms were introduced， followed by a survey of the existing challenges of DNAS. Then， a hierarchical analysis of current DNAS algorithms was conducted. Finally， the future research directions of DNAS were discussed.