This paper reviews the research work of Artificial Neural Network（ANN）and Deep Neural Network（DNN）for extracting temperature distribution information in Brillouin Optical Time Domain Analyzer（BOTDA）sensing system.After proper training on ANN or DNN model

the Brillouin gain along the measured temperature distribution of optical fiber can be extracted from the Brillouin Gain Spectrum（BGS）

rather than the traditional Lorenz Curve Fitting（LCF）method which fits the BGS as Brillouin Frequency Shift（BFS）

and then convert it to the temperature information.The experimental results show the advantages of using ANN and DNN for temperature extraction compared with the traditional LCF method.